continuous.py

#   Copyright 2020 The PyMC Developers
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

# coding: utf-8
"""
A collection of common probability distributions for stochastic
nodes in PyMC.
"""

from typing import Union

import aesara.tensor as at
import numpy as np

from aesara.assert_op import Assert
from aesara.tensor.random.basic import (
    BetaRV,
    cauchy,
    exponential,
    gamma,
    gumbel,
    halfcauchy,
    halfnormal,
    invgamma,
    normal,
    pareto,
    uniform,
    weibull,
)
from aesara.tensor.random.op import RandomVariable
from aesara.tensor.var import TensorVariable
from scipy import stats
from scipy.interpolate import InterpolatedUnivariateSpline

from pymc3.aesaraf import floatX
from pymc3.distributions import logp_transform, transforms
from pymc3.distributions.dist_math import (
    SplineWrapper,
    betaln,
    bound,
    clipped_beta_rvs,
    gammaln,
    i0e,
    incomplete_beta,
    log_normal,
    logpow,
    normal_lccdf,
    normal_lcdf,
    zvalue,
)
from pymc3.distributions.distribution import Continuous
from pymc3.distributions.special import log_i0
from pymc3.math import invlogit, log1mexp, log1pexp, logdiffexp, logit

__all__ = [
    "Uniform",
    "Flat",
    "HalfFlat",
    "Normal",
    "TruncatedNormal",
    "Beta",
    "Kumaraswamy",
    "Exponential",
    "Laplace",
    "StudentT",
    "Cauchy",
    "HalfCauchy",
    "Gamma",
    "Weibull",
    "HalfStudentT",
    "Lognormal",
    "ChiSquared",
    "HalfNormal",
    "Wald",
    "Pareto",
    "InverseGamma",
    "ExGaussian",
    "VonMises",
    "SkewNormal",
    "Triangular",
    "Gumbel",
    "Logistic",
    "LogitNormal",
    "Interpolated",
    "Rice",
    "Moyal",
    "AsymmetricLaplace",
]


class PositiveContinuous(Continuous):
    """Base class for positive continuous distributions"""


class UnitContinuous(Continuous):
    """Base class for continuous distributions on [0,1]"""


class BoundedContinuous(Continuous):
    """Base class for bounded continuous distributions"""


@logp_transform.register(PositiveContinuous)
def pos_cont_transform(op):
    return transforms.log


@logp_transform.register(UnitContinuous)
def unit_cont_transform(op):
    return transforms.logodds


@logp_transform.register(BoundedContinuous)
def bounded_cont_transform(op):
    def transform_params(rv_var):
        _, _, _, lower, upper = rv_var.owner.inputs
        lower = at.as_tensor_variable(lower) if lower is not None else None
        upper = at.as_tensor_variable(upper) if upper is not None else None
        return lower, upper

    return transforms.interval(transform_params)


def assert_negative_support(var, label, distname, value=-1e-6):
    msg = f"The variable specified for {label} has negative support for {distname}, "
    msg += "likely making it unsuitable for this parameter."
    return Assert(msg)(var, at.all(at.ge(var, 0.0)))


def get_tau_sigma(tau=None, sigma=None):
    r"""
    Find precision and standard deviation. The link between the two
    parameterizations is given by the inverse relationship:

    .. math::
        \tau = \frac{1}{\sigma^2}

    Parameters
    ----------
    tau: array-like, optional
    sigma: array-like, optional

    Results
    -------
    Returns tuple (tau, sigma)

    Notes
    -----
    If neither tau nor sigma is provided, returns (1., 1.)
    """
    if tau is None:
        if sigma is None:
            sigma = 1.0
            tau = 1.0
        else:
            tau = sigma ** -2.0

    else:
        if sigma is not None:
            raise ValueError("Can't pass both tau and sigma")
        else:
            sigma = tau ** -0.5

    return floatX(tau), floatX(sigma)


class Uniform(BoundedContinuous):
    r"""
    Continuous uniform log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid lower, upper) = \frac{1}{upper-lower}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-3, 3, 500)
        ls = [0., -2]
        us = [2., 1]
        for l, u in zip(ls, us):
            y = np.zeros(500)
            y[(x<u) & (x>l)] = 1.0/(u-l)
            plt.plot(x, y, label='lower = {}, upper = {}'.format(l, u))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.ylim(0, 1)
        plt.legend(loc=1)
        plt.show()

    ========  =====================================
    Support   :math:`x \in [lower, upper]`
    Mean      :math:`\dfrac{lower + upper}{2}`
    Variance  :math:`\dfrac{(upper - lower)^2}{12}`
    ========  =====================================

    Parameters
    ----------
    lower: float
        Lower limit.
    upper: float
        Upper limit.
    """
    rv_op = uniform

    @classmethod
    def dist(cls, lower=0, upper=1, **kwargs):
        lower = at.as_tensor_variable(floatX(lower))
        upper = at.as_tensor_variable(floatX(upper))
        # mean = (upper + lower) / 2.0
        # median = self.mean
        return super().dist([lower, upper], **kwargs)

    def logp(value, lower, upper):
        """
        Calculate log-probability of Uniform distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value for which log-probability is calculated.

        Returns
        -------
        TensorVariable
        """
        return bound(-at.log(upper - lower), value >= lower, value <= upper)

    def logcdf(value, lower, upper):
        """
        Compute the log of the cumulative distribution function for Uniform distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or `TensorVariable`
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or `TensorVariable`.

        Returns
        -------
        TensorVariable
        """
        return at.switch(
            at.lt(value, lower) | at.lt(upper, lower),
            -np.inf,
            at.switch(
                at.lt(value, upper),
                at.log(value - lower) - at.log(upper - lower),
                0,
            ),
        )


class Flat(Continuous):
    """
    Uninformative log-likelihood that returns 0 regardless of
    the passed value.
    """

    def __init__(self, *args, **kwargs):
        self._default = 0
        super().__init__(defaults=("_default",), *args, **kwargs)

    def random(self, point=None, size=None):
        """Raises ValueError as it is not possible to sample from Flat distribution

        Parameters
        ----------
        point: dict, optional
        size: int, optional

        Raises
        -------
        ValueError
        """
        raise ValueError("Cannot sample from Flat distribution")

    def logp(self, value):
        """
        Calculate log-probability of Flat distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return at.zeros_like(value)

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Flat distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        return at.switch(
            at.eq(value, -np.inf), -np.inf, at.switch(at.eq(value, np.inf), 0, at.log(0.5))
        )


class HalfFlat(PositiveContinuous):
    """Improper flat prior over the positive reals."""

    def __init__(self, *args, **kwargs):
        self._default = 1
        super().__init__(defaults=("_default",), *args, **kwargs)

    def random(self, point=None, size=None):
        """Raises ValueError as it is not possible to sample from HalfFlat distribution

        Parameters
        ----------
        point: dict, optional
        size: int, optional

        Raises
        -------
        ValueError
        """
        raise ValueError("Cannot sample from HalfFlat distribution")

    def logp(self, value):
        """
        Calculate log-probability of HalfFlat distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return bound(at.zeros_like(value), value > 0)

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for HalfFlat distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        return at.switch(at.lt(value, np.inf), -np.inf, at.switch(at.eq(value, np.inf), 0, -np.inf))


class Normal(Continuous):
    r"""
    Univariate normal log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, \tau) =
           \sqrt{\frac{\tau}{2\pi}}
           \exp\left\{ -\frac{\tau}{2} (x-\mu)^2 \right\}

    Normal distribution can be parameterized either in terms of precision
    or standard deviation. The link between the two parametrizations is
    given by

    .. math::

       \tau = \dfrac{1}{\sigma^2}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-5, 5, 1000)
        mus = [0., 0., 0., -2.]
        sigmas = [0.4, 1., 2., 0.4]
        for mu, sigma in zip(mus, sigmas):
            pdf = st.norm.pdf(x, mu, sigma)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}'.format(mu, sigma))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu`
    Variance  :math:`\dfrac{1}{\tau}` or :math:`\sigma^2`
    ========  ==========================================

    Parameters
    ----------
    mu: float
        Mean.
    sigma: float
        Standard deviation (sigma > 0) (only required if tau is not specified).
    tau: float
        Precision (tau > 0) (only required if sigma is not specified).

    Examples
    --------
    .. code-block:: python

        with pm.Model():
            x = pm.Normal('x', mu=0, sigma=10)

        with pm.Model():
            x = pm.Normal('x', mu=0, tau=1/23)
    """
    rv_op = normal

    @classmethod
    def dist(cls, mu=0, sigma=None, tau=None, sd=None, no_assert=False, **kwargs):
        if sd is not None:
            sigma = sd
        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
        sigma = at.as_tensor_variable(sigma)

        # sd = sigma
        # tau = at.as_tensor_variable(tau)
        # mean = median = mode = mu = at.as_tensor_variable(floatX(mu))
        # variance = 1.0 / self.tau

        if not no_assert:
            assert_negative_support(sigma, "sigma", "Normal")

        return super().dist([mu, sigma], **kwargs)

    def logp(value, mu, sigma):
        """
        Calculate log-probability of Normal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or `TensorVariable`.

        Returns
        -------
        TensorVariable
        """
        tau, sigma = get_tau_sigma(tau=None, sigma=sigma)

        return bound((-tau * (value - mu) ** 2 + at.log(tau / np.pi / 2.0)) / 2.0, sigma > 0)

    def logcdf(value, mu, sigma):
        """
        Compute the log of the cumulative distribution function for Normal distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or `TensorVariable`
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or `TensorVariable`.

        Returns
        -------
        TensorVariable
        """
        return bound(
            normal_lcdf(mu, sigma, value),
            0 < sigma,
        )


class TruncatedNormal(BoundedContinuous):
    r"""
    Univariate truncated normal log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x;\mu ,\sigma ,a,b)={\frac {\phi ({\frac {x-\mu }{\sigma }})}{
       \sigma \left(\Phi ({\frac {b-\mu }{\sigma }})-\Phi ({\frac {a-\mu }{\sigma }})\right)}}

    Truncated normal distribution can be parameterized either in terms of precision
    or standard deviation. The link between the two parametrizations is
    given by

    .. math::

       \tau = \dfrac{1}{\sigma^2}


    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-10, 10, 1000)
        mus = [0.,  0., 0.]
        sigmas = [3.,5.,7.]
        a1 = [-3, -5, -5]
        b1 = [7, 5, 4]
        for mu, sigma, a, b in zip(mus, sigmas,a1,b1):
            an, bn = (a - mu) / sigma, (b - mu) / sigma
            pdf = st.truncnorm.pdf(x, an,bn, loc=mu, scale=sigma)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}, a={}, b={}'.format(mu, sigma, a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in [a, b]`
    Mean      :math:`\mu +{\frac {\phi (\alpha )-\phi (\beta )}{Z}}\sigma`
    Variance  :math:`\sigma ^{2}\left[1+{\frac {\alpha \phi (\alpha )-\beta \phi (\beta )}{Z}}-\left({\frac {\phi (\alpha )-\phi (\beta )}{Z}}\right)^{2}\right]`
    ========  ==========================================

    Parameters
    ----------
    mu: float
        Mean.
    sigma: float
        Standard deviation (sigma > 0).
    lower: float (optional)
        Left bound.
    upper: float (optional)
        Right bound.

    Examples
    --------
    .. code-block:: python

        with pm.Model():
            x = pm.TruncatedNormal('x', mu=0, sigma=10, lower=0)

        with pm.Model():
            x = pm.TruncatedNormal('x', mu=0, sigma=10, upper=1)

        with pm.Model():
            x = pm.TruncatedNormal('x', mu=0, sigma=10, lower=0, upper=1)

    """

    def __init__(
        self,
        mu=0,
        sigma=None,
        tau=None,
        lower=None,
        upper=None,
        transform="auto",
        sd=None,
        *args,
        **kwargs,
    ):
        if sd is not None:
            sigma = sd
        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
        self.sigma = self.sd = at.as_tensor_variable(sigma)
        self.tau = at.as_tensor_variable(tau)
        self.lower_check = at.as_tensor_variable(floatX(lower)) if lower is not None else lower
        self.upper_check = at.as_tensor_variable(floatX(upper)) if upper is not None else upper
        self.lower = (
            at.as_tensor_variable(floatX(lower))
            if lower is not None
            else at.as_tensor_variable(-np.inf)
        )
        self.upper = (
            at.as_tensor_variable(floatX(upper))
            if upper is not None
            else at.as_tensor_variable(np.inf)
        )
        self.mu = at.as_tensor_variable(floatX(mu))

        if self.lower_check is None and self.upper_check is None:
            self._defaultval = mu
        elif self.lower_check is None and self.upper_check is not None:
            self._defaultval = self.upper - 1.0
        elif self.lower_check is not None and self.upper_check is None:
            self._defaultval = self.lower + 1.0
        else:
            self._defaultval = (self.lower + self.upper) / 2

        assert_negative_support(sigma, "sigma", "TruncatedNormal")
        assert_negative_support(tau, "tau", "TruncatedNormal")

        super().__init__(
            defaults=("_defaultval",),
            transform=transform,
            lower=lower,
            upper=upper,
            *args,
            **kwargs,
        )

    def random(self, point=None, size=None):
        """
        Draw random values from TruncatedNormal distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, sigma, lower, upper = draw_values(
        #     [self.mu, self.sigma, self.lower, self.upper], point=point, size=size
        # )
        # return generate_samples(
        #     self._random,
        #     mu=mu,
        #     sigma=sigma,
        #     lower=lower,
        #     upper=upper,
        #     dist_shape=self.shape,
        #     size=size,
        # )

    def _random(self, mu, sigma, lower, upper, size):
        """Wrapper around stats.truncnorm.rvs that converts TruncatedNormal's
        parametrization to scipy.truncnorm. All parameter arrays should have
        been broadcasted properly by generate_samples at this point and size is
        the scipy.rvs representation.
        """
        return stats.truncnorm.rvs(
            a=(lower - mu) / sigma, b=(upper - mu) / sigma, loc=mu, scale=sigma, size=size
        )

    def logp(self, value):
        """
        Calculate log-probability of TruncatedNormal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma

        norm = self._normalization()
        logp = Normal.dist(mu=mu, sigma=sigma).logp(value) - norm

        bounds = [sigma > 0]
        if self.lower_check is not None:
            bounds.append(value >= self.lower)
        if self.upper_check is not None:
            bounds.append(value <= self.upper)
        return bound(logp, *bounds)

    def _normalization(self):
        mu, sigma = self.mu, self.sigma

        if self.lower_check is None and self.upper_check is None:
            return 0.0

        if self.lower_check is not None and self.upper_check is not None:
            lcdf_a = normal_lcdf(mu, sigma, self.lower)
            lcdf_b = normal_lcdf(mu, sigma, self.upper)
            lsf_a = normal_lccdf(mu, sigma, self.lower)
            lsf_b = normal_lccdf(mu, sigma, self.upper)

            return at.switch(self.lower > 0, logdiffexp(lsf_a, lsf_b), logdiffexp(lcdf_b, lcdf_a))

        if self.lower_check is not None:
            return normal_lccdf(mu, sigma, self.lower)
        else:
            return normal_lcdf(mu, sigma, self.upper)

    def _distr_parameters_for_repr(self):
        return ["mu", "sigma", "lower", "upper"]


class HalfNormal(PositiveContinuous):
    r"""
    Half-normal log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \tau) =
           \sqrt{\frac{2\tau}{\pi}}
           \exp\left(\frac{-x^2 \tau}{2}\right)

       f(x \mid \sigma) =
           \sqrt{\frac{2}{\pi\sigma^2}}
           \exp\left(\frac{-x^2}{2\sigma^2}\right)

    .. note::

       The parameters ``sigma``/``tau`` (:math:`\sigma`/:math:`\tau`) refer to
       the standard deviation/precision of the unfolded normal distribution, for
       the standard deviation of the half-normal distribution, see below. For
       the half-normal, they are just two parameterisation :math:`\sigma^2
       \equiv \frac{1}{\tau}` of a scale parameter

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 5, 200)
        for sigma in [0.4, 1., 2.]:
            pdf = st.halfnorm.pdf(x, scale=sigma)
            plt.plot(x, pdf, label=r'$\sigma$ = {}'.format(sigma))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in [0, \infty)`
    Mean      :math:`\sqrt{\dfrac{2}{\tau \pi}}` or :math:`\dfrac{\sigma \sqrt{2}}{\sqrt{\pi}}`
    Variance  :math:`\dfrac{1}{\tau}\left(1 - \dfrac{2}{\pi}\right)` or :math:`\sigma^2\left(1 - \dfrac{2}{\pi}\right)`
    ========  ==========================================

    Parameters
    ----------
    sigma: float
        Scale parameter :math:`sigma` (``sigma`` > 0) (only required if ``tau`` is not specified).
    tau: float
        Precision :math:`tau` (tau > 0) (only required if sigma is not specified).

    Examples
    --------
    .. code-block:: python

        with pm.Model():
            x = pm.HalfNormal('x', sigma=10)

        with pm.Model():
            x = pm.HalfNormal('x', tau=1/15)
    """
    rv_op = halfnormal

    @classmethod
    def dist(cls, sigma=None, tau=None, sd=None, *args, **kwargs):
        if sd is not None:
            sigma = sd

        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)

        assert_negative_support(tau, "tau", "HalfNormal")
        assert_negative_support(sigma, "sigma", "HalfNormal")

        return super().dist([0.0, sigma], **kwargs)

    def logp(value, loc, sigma):
        """
        Calculate log-probability of HalfNormal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        tau, sigma = get_tau_sigma(tau=None, sigma=sigma)

        return bound(
            -0.5 * tau * (value - loc) ** 2 + 0.5 * at.log(tau * 2.0 / np.pi),
            value >= loc,
            tau > 0,
            sigma > 0,
        )

    def logcdf(value, loc, sigma):
        """
        Compute the log of the cumulative distribution function for HalfNormal distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        z = zvalue(value, mu=loc, sigma=sigma)
        return bound(
            at.log1p(-at.erfc(z / at.sqrt(2.0))),
            loc <= value,
            0 < sigma,
        )

    def _distr_parameters_for_repr(self):
        return ["sigma"]


class Wald(PositiveContinuous):
    r"""
    Wald log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, \lambda) =
           \left(\frac{\lambda}{2\pi}\right)^{1/2} x^{-3/2}
           \exp\left\{
               -\frac{\lambda}{2x}\left(\frac{x-\mu}{\mu}\right)^2
           \right\}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 3, 500)
        mus = [1., 1., 1., 3.]
        lams = [1., .2, 3., 1.]
        for mu, lam in zip(mus, lams):
            pdf = st.invgauss.pdf(x, mu/lam, scale=lam)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\lambda$ = {}'.format(mu, lam))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  =============================
    Support   :math:`x \in (0, \infty)`
    Mean      :math:`\mu`
    Variance  :math:`\dfrac{\mu^3}{\lambda}`
    ========  =============================

    Wald distribution can be parameterized either in terms of lam or phi.
    The link between the two parametrizations is given by

    .. math::

       \phi = \dfrac{\lambda}{\mu}

    Parameters
    ----------
    mu: float, optional
        Mean of the distribution (mu > 0).
    lam: float, optional
        Relative precision (lam > 0).
    phi: float, optional
        Alternative shape parameter (phi > 0).
    alpha: float, optional
        Shift/location parameter (alpha >= 0).

    Notes
    -----
    To instantiate the distribution specify any of the following

    - only mu (in this case lam will be 1)
    - mu and lam
    - mu and phi
    - lam and phi

    References
    ----------
    .. [Tweedie1957] Tweedie, M. C. K. (1957).
       Statistical Properties of Inverse Gaussian Distributions I.
       The Annals of Mathematical Statistics, Vol. 28, No. 2, pp. 362-377

    .. [Michael1976] Michael, J. R., Schucany, W. R. and Hass, R. W. (1976).
        Generating Random Variates Using Transformations with Multiple Roots.
        The American Statistician, Vol. 30, No. 2, pp. 88-90

    .. [Giner2016] Göknur Giner, Gordon K. Smyth (2016)
       statmod: Probability Calculations for the Inverse Gaussian Distribution
    """

    def __init__(self, mu=None, lam=None, phi=None, alpha=0.0, *args, **kwargs):
        super().__init__(*args, **kwargs)
        mu, lam, phi = self.get_mu_lam_phi(mu, lam, phi)
        self.alpha = alpha = at.as_tensor_variable(floatX(alpha))
        self.mu = mu = at.as_tensor_variable(floatX(mu))
        self.lam = lam = at.as_tensor_variable(floatX(lam))
        self.phi = phi = at.as_tensor_variable(floatX(phi))

        self.mean = self.mu + self.alpha
        self.mode = (
            self.mu * (at.sqrt(1.0 + (1.5 * self.mu / self.lam) ** 2) - 1.5 * self.mu / self.lam)
            + self.alpha
        )
        self.variance = (self.mu ** 3) / self.lam

        assert_negative_support(phi, "phi", "Wald")
        assert_negative_support(mu, "mu", "Wald")
        assert_negative_support(lam, "lam", "Wald")

    def get_mu_lam_phi(self, mu, lam, phi):
        if mu is None:
            if lam is not None and phi is not None:
                return lam / phi, lam, phi
        else:
            if lam is None:
                if phi is None:
                    return mu, 1.0, 1.0 / mu
                else:
                    return mu, mu * phi, phi
            else:
                if phi is None:
                    return mu, lam, lam / mu

        raise ValueError(
            "Wald distribution must specify either mu only, "
            "mu and lam, mu and phi, or lam and phi."
        )

    def _random(self, mu, lam, alpha, size=None):
        v = np.random.normal(size=size) ** 2
        value = (
            mu
            + (mu ** 2) * v / (2.0 * lam)
            - mu / (2.0 * lam) * np.sqrt(4.0 * mu * lam * v + (mu * v) ** 2)
        )
        z = np.random.uniform(size=size)
        i = np.floor(z - mu / (mu + value)) * 2 + 1
        value = (value ** -i) * (mu ** (i + 1))
        return value + alpha

    def random(self, point=None, size=None):
        """
        Draw random values from Wald distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, lam, alpha = draw_values([self.mu, self.lam, self.alpha], point=point, size=size)
        # return generate_samples(self._random, mu, lam, alpha, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Wald distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        lam = self.lam
        alpha = self.alpha
        centered_value = value - alpha
        # value *must* be iid. Otherwise this is wrong.
        return bound(
            logpow(lam / (2.0 * np.pi), 0.5)
            - logpow(centered_value, 1.5)
            - (0.5 * lam / centered_value * ((centered_value - mu) / mu) ** 2),
            centered_value > 0,
            mu > 0,
            lam > 0,
            alpha >= 0,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "lam", "alpha"]

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Wald distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        # Distribution parameters
        mu = self.mu
        lam = self.lam
        alpha = self.alpha

        value -= alpha
        q = value / mu
        l = lam * mu
        r = at.sqrt(value * lam)

        a = normal_lcdf(0, 1, (q - 1.0) / r)
        b = 2.0 / l + normal_lcdf(0, 1, -(q + 1.0) / r)

        return bound(
            at.switch(
                at.lt(value, np.inf),
                a + log1pexp(b - a),
                0,
            ),
            0 < value,
            0 < mu,
            0 < lam,
            0 <= alpha,
        )


class BetaClippedRV(BetaRV):
    @classmethod
    def rng_fn(cls, rng, alpha, beta, size):
        return clipped_beta_rvs(alpha, beta, size=size, random_state=rng)


beta = BetaClippedRV()


class Beta(UnitContinuous):
    r"""
    Beta log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, \beta) =
           \frac{x^{\alpha - 1} (1 - x)^{\beta - 1}}{B(\alpha, \beta)}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 1, 200)
        alphas = [.5, 5., 1., 2., 2.]
        betas = [.5, 1., 3., 2., 5.]
        for a, b in zip(alphas, betas):
            pdf = st.beta.pdf(x, a, b)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.ylim(0, 4.5)
        plt.legend(loc=9)
        plt.show()

    ========  ==============================================================
    Support   :math:`x \in (0, 1)`
    Mean      :math:`\dfrac{\alpha}{\alpha + \beta}`
    Variance  :math:`\dfrac{\alpha \beta}{(\alpha+\beta)^2(\alpha+\beta+1)}`
    ========  ==============================================================

    Beta distribution can be parameterized either in terms of alpha and
    beta or mean and standard deviation. The link between the two
    parametrizations is given by

    .. math::

       \alpha &= \mu \kappa \\
       \beta  &= (1 - \mu) \kappa

       \text{where } \kappa = \frac{\mu(1-\mu)}{\sigma^2} - 1

    Parameters
    ----------
    alpha: float
        alpha > 0.
    beta: float
        beta > 0.
    mu: float
        Alternative mean (0 < mu < 1).
    sigma: float
        Alternative standard deviation (0 < sigma < sqrt(mu * (1 - mu))).

    Notes
    -----
    Beta distribution is a conjugate prior for the parameter :math:`p` of
    the binomial distribution.
    """

    rv_op = beta

    @classmethod
    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwargs):
        if sd is not None:
            sigma = sd

        alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
        alpha = at.as_tensor_variable(floatX(alpha))
        beta = at.as_tensor_variable(floatX(beta))

        assert_negative_support(alpha, "alpha", "Beta")
        assert_negative_support(beta, "beta", "Beta")

        return super().dist([alpha, beta], **kwargs)

    @classmethod
    def get_alpha_beta(self, alpha=None, beta=None, mu=None, sigma=None):
        if (alpha is not None) and (beta is not None):
            pass
        elif (mu is not None) and (sigma is not None):
            kappa = mu * (1 - mu) / sigma ** 2 - 1
            alpha = mu * kappa
            beta = (1 - mu) * kappa
        else:
            raise ValueError(
                "Incompatible parameterization. Either use alpha "
                "and beta, or mu and sigma to specify distribution."
            )

        return alpha, beta

    def _distr_parameters_for_repr(self):
        return ["alpha", "beta"]

    def logp(value, alpha, beta):
        """
        Calculate log-probability of Beta distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """

        logval = at.log(value)
        log1pval = at.log1p(-value)
        logp = (
            at.switch(at.eq(alpha, 1), 0, (alpha - 1) * logval)
            + at.switch(at.eq(beta, 1), 0, (beta - 1) * log1pval)
            - betaln(alpha, beta)
        )

        return bound(logp, value >= 0, value <= 1, alpha > 0, beta > 0)

    def logcdf(value, alpha, beta):
        """
        Compute the log of the cumulative distribution function for Beta distribution
        at the specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log CDF is calculated.

        Returns
        -------
        TensorVariable
        """
        # incomplete_beta function can only handle scalar values (see #4342)
        if np.ndim(value):
            raise TypeError(
                f"Beta.logcdf expects a scalar value but received a {np.ndim(value)}-dimensional object."
            )

        return bound(
            at.switch(
                at.lt(value, 1),
                at.log(incomplete_beta(alpha, beta, value)),
                0,
            ),
            0 <= value,
            0 < alpha,
            0 < beta,
        )


class Kumaraswamy(UnitContinuous):
    r"""
    Kumaraswamy log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid a, b) =
           abx^{a-1}(1-x^a)^{b-1}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 1, 200)
        a_s = [.5, 5., 1., 2., 2.]
        b_s = [.5, 1., 3., 2., 5.]
        for a, b in zip(a_s, b_s):
            pdf = a * b * x ** (a - 1) * (1 - x ** a) ** (b - 1)
            plt.plot(x, pdf, label=r'$a$ = {}, $b$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.ylim(0, 3.)
        plt.legend(loc=9)
        plt.show()

    ========  ==============================================================
    Support   :math:`x \in (0, 1)`
    Mean      :math:`b B(1 + \tfrac{1}{a}, b)`
    Variance  :math:`b B(1 + \tfrac{2}{a}, b) - (b B(1 + \tfrac{1}{a}, b))^2`
    ========  ==============================================================

    Parameters
    ----------
    a: float
        a > 0.
    b: float
        b > 0.
    """

    def __init__(self, a, b, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.a = a = at.as_tensor_variable(floatX(a))
        self.b = b = at.as_tensor_variable(floatX(b))

        ln_mean = at.log(b) + at.gammaln(1 + 1 / a) + at.gammaln(b) - at.gammaln(1 + 1 / a + b)
        self.mean = at.exp(ln_mean)
        ln_2nd_raw_moment = (
            at.log(b) + at.gammaln(1 + 2 / a) + at.gammaln(b) - at.gammaln(1 + 2 / a + b)
        )
        self.variance = at.exp(ln_2nd_raw_moment) - self.mean ** 2

        assert_negative_support(a, "a", "Kumaraswamy")
        assert_negative_support(b, "b", "Kumaraswamy")

    def _random(self, a, b, size=None):
        u = np.random.uniform(size=size)
        return (1 - (1 - u) ** (1 / b)) ** (1 / a)

    def random(self, point=None, size=None):
        """
        Draw random values from Kumaraswamy distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # a, b = draw_values([self.a, self.b], point=point, size=size)
        # return generate_samples(self._random, a, b, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Kumaraswamy distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        a = self.a
        b = self.b

        logp = at.log(a) + at.log(b) + (a - 1) * at.log(value) + (b - 1) * at.log(1 - value ** a)

        return bound(logp, value >= 0, value <= 1, a > 0, b > 0)


class Exponential(PositiveContinuous):
    r"""
    Exponential log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \lambda) = \lambda \exp\left\{ -\lambda x \right\}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 3, 100)
        for lam in [0.5, 1., 2.]:
            pdf = st.expon.pdf(x, scale=1.0/lam)
            plt.plot(x, pdf, label=r'$\lambda$ = {}'.format(lam))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ============================
    Support   :math:`x \in [0, \infty)`
    Mean      :math:`\dfrac{1}{\lambda}`
    Variance  :math:`\dfrac{1}{\lambda^2}`
    ========  ============================

    Parameters
    ----------
    lam: float
        Rate or inverse scale (lam > 0)
    """
    rv_op = exponential

    @classmethod
    def dist(cls, lam, *args, **kwargs):
        lam = at.as_tensor_variable(floatX(lam))

        assert_negative_support(lam, "lam", "Exponential")

        # Aesara exponential op is parametrized in terms of mu (1/lam)
        return super().dist([at.inv(lam)], **kwargs)

    def logp(value, mu):
        """
        Calculate log-probability of Exponential distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log
            probabilities for multiple values are desired the values must be
            provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        lam = at.inv(mu)
        return bound(
            at.log(lam) - lam * value,
            value >= 0,
            lam > 0,
        )

    def logcdf(value, mu):
        r"""
        Compute the log of cumulative distribution function for the Exponential distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for
            multiple values are desired the values must be provided in a numpy
            array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        lam = at.inv(mu)
        return bound(
            log1mexp(lam * value),
            0 <= value,
            0 <= lam,
        )


class Laplace(Continuous):
    r"""
    Laplace log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, b) =
           \frac{1}{2b} \exp \left\{ - \frac{|x - \mu|}{b} \right\}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-10, 10, 1000)
        mus = [0., 0., 0., -5.]
        bs = [1., 2., 4., 4.]
        for mu, b in zip(mus, bs):
            pdf = st.laplace.pdf(x, loc=mu, scale=b)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $b$ = {}'.format(mu, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu`
    Variance  :math:`2 b^2`
    ========  ========================

    Parameters
    ----------
    mu: float
        Location parameter.
    b: float
        Scale parameter (b > 0).
    """

    def __init__(self, mu, b, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.b = b = at.as_tensor_variable(floatX(b))
        self.mean = self.median = self.mode = self.mu = mu = at.as_tensor_variable(floatX(mu))

        self.variance = 2 * self.b ** 2

        assert_negative_support(b, "b", "Laplace")

    def random(self, point=None, size=None):
        """
        Draw random values from Laplace distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, b = draw_values([self.mu, self.b], point=point, size=size)
        # return generate_samples(np.random.laplace, mu, b, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Laplace distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        b = self.b

        return -at.log(2 * b) - abs(value - mu) / b

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Laplace distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        a = self.mu
        b = self.b
        y = (value - a) / b
        return bound(
            at.switch(
                at.le(value, a),
                at.log(0.5) + y,
                at.switch(
                    at.gt(y, 1),
                    at.log1p(-0.5 * at.exp(-y)),
                    at.log(1 - 0.5 * at.exp(-y)),
                ),
            ),
            0 < b,
        )


class AsymmetricLaplace(Continuous):
    r"""
    Asymmetric-Laplace log-likelihood.

    The pdf of this distribution is

    .. math::
        {f(x|\\b,\kappa,\mu) =
            \left({\frac{\\b}{\kappa + 1/\kappa}}\right)\,e^{-(x-\mu)\\b\,s\kappa ^{s}}}

    where

    .. math::

        s = sgn(x-\mu)

    ========  ========================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu-\frac{\\\kappa-1/\kappa}b`
    Variance  :math:`\frac{1+\kappa^{4}}{b^2\kappa^2 }`
    ========  ========================

    Parameters
    ----------
    b: float
        Scale parameter (b > 0)
    kappa: float
        Symmetry parameter (kappa > 0)
    mu: float
        Location parameter

    See Also:
    --------
    `Reference <https://en.wikipedia.org/wiki/Asymmetric_Laplace_distribution>`_
    """

    def __init__(self, b, kappa, mu=0, *args, **kwargs):
        self.b = at.as_tensor_variable(floatX(b))
        self.kappa = at.as_tensor_variable(floatX(kappa))
        self.mu = mu = at.as_tensor_variable(floatX(mu))

        self.mean = self.mu - (self.kappa - 1 / self.kappa) / b
        self.variance = (1 + self.kappa ** 4) / (self.kappa ** 2 * self.b ** 2)

        assert_negative_support(kappa, "kappa", "AsymmetricLaplace")
        assert_negative_support(b, "b", "AsymmetricLaplace")

        super().__init__(*args, **kwargs)

    def _random(self, b, kappa, mu, size=None):
        u = np.random.uniform(size=size)
        switch = kappa ** 2 / (1 + kappa ** 2)
        non_positive_x = mu + kappa * np.log(u * (1 / switch)) / b
        positive_x = mu - np.log((1 - u) * (1 + kappa ** 2)) / (kappa * b)
        draws = non_positive_x * (u <= switch) + positive_x * (u > switch)
        return draws

    def random(self, point=None, size=None):
        """
        Draw random samples from this distribution, using the inverse CDF method.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size:int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # b, kappa, mu = draw_values([self.b, self.kappa, self.mu], point=point, size=size)
        # return generate_samples(self._random, b, kappa, mu, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Asymmetric-Laplace distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        value = value - self.mu
        return bound(
            at.log(self.b / (self.kappa + (self.kappa ** -1)))
            + (-value * self.b * at.sgn(value) * (self.kappa ** at.sgn(value))),
            0 < self.b,
            0 < self.kappa,
        )


class Lognormal(PositiveContinuous):
    r"""
    Log-normal log-likelihood.

    Distribution of any random variable whose logarithm is normally
    distributed. A variable might be modeled as log-normal if it can
    be thought of as the multiplicative product of many small
    independent factors.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, \tau) =
           \frac{1}{x} \sqrt{\frac{\tau}{2\pi}}
           \exp\left\{ -\frac{\tau}{2} (\ln(x)-\mu)^2 \right\}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 3, 100)
        mus = [0., 0., 0.]
        sigmas = [.25, .5, 1.]
        for mu, sigma in zip(mus, sigmas):
            pdf = st.lognorm.pdf(x, sigma, scale=np.exp(mu))
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}'.format(mu, sigma))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  =========================================================================
    Support   :math:`x \in [0, \infty)`
    Mean      :math:`\exp\{\mu + \frac{1}{2\tau}\}`
    Variance  :math:`(\exp\{\frac{1}{\tau}\} - 1) \times \exp\{2\mu + \frac{1}{\tau}\}`
    ========  =========================================================================

    Parameters
    ----------
    mu: float
        Location parameter.
    sigma: float
        Standard deviation. (sigma > 0). (only required if tau is not specified).
    tau: float
        Scale parameter (tau > 0). (only required if sigma is not specified).

    Examples
    --------

    .. code-block:: python

        # Example to show that we pass in only ``sigma`` or ``tau`` but not both.
        with pm.Model():
            x = pm.Lognormal('x', mu=2, sigma=30)

        with pm.Model():
            x = pm.Lognormal('x', mu=2, tau=1/100)
    """

    def __init__(self, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if sd is not None:
            sigma = sd

        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)

        self.mu = mu = at.as_tensor_variable(floatX(mu))
        self.tau = tau = at.as_tensor_variable(tau)
        self.sigma = self.sd = sigma = at.as_tensor_variable(sigma)

        self.mean = at.exp(self.mu + 1.0 / (2 * self.tau))
        self.median = at.exp(self.mu)
        self.mode = at.exp(self.mu - 1.0 / self.tau)
        self.variance = (at.exp(1.0 / self.tau) - 1) * at.exp(2 * self.mu + 1.0 / self.tau)

        assert_negative_support(tau, "tau", "Lognormal")
        assert_negative_support(sigma, "sigma", "Lognormal")

    def _random(self, mu, tau, size=None):
        samples = np.random.normal(size=size)
        return np.exp(mu + (tau ** -0.5) * samples)

    def random(self, point=None, size=None):
        """
        Draw random values from Lognormal distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, tau = draw_values([self.mu, self.tau], point=point, size=size)
        # return generate_samples(self._random, mu, tau, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Lognormal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        tau = self.tau
        return bound(
            -0.5 * tau * (at.log(value) - mu) ** 2
            + 0.5 * at.log(tau / (2.0 * np.pi))
            - at.log(value),
            tau > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "tau"]

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Lognormal distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma
        tau = self.tau

        return bound(
            normal_lcdf(mu, sigma, at.log(value)),
            0 < value,
            0 < tau,
        )


class StudentT(Continuous):
    r"""
    Student's T log-likelihood.

    Describes a normal variable whose precision is gamma distributed.
    If only nu parameter is passed, this specifies a standard (central)
    Student's T.

    The pdf of this distribution is

    .. math::

       f(x|\mu,\lambda,\nu) =
           \frac{\Gamma(\frac{\nu + 1}{2})}{\Gamma(\frac{\nu}{2})}
           \left(\frac{\lambda}{\pi\nu}\right)^{\frac{1}{2}}
           \left[1+\frac{\lambda(x-\mu)^2}{\nu}\right]^{-\frac{\nu+1}{2}}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-8, 8, 200)
        mus = [0., 0., -2., -2.]
        sigmas = [1., 1., 1., 2.]
        dfs = [1., 5., 5., 5.]
        for mu, sigma, df in zip(mus, sigmas, dfs):
            pdf = st.t.pdf(x, df, loc=mu, scale=sigma)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}, $\nu$ = {}'.format(mu, sigma, df))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in \mathbb{R}`
    ========  ========================

    Parameters
    ----------
    nu: float
        Degrees of freedom, also known as normality parameter (nu > 0).
    mu: float
        Location parameter.
    sigma: float
        Scale parameter (sigma > 0). Converges to the standard deviation as nu
        increases. (only required if lam is not specified)
    lam: float
        Scale parameter (lam > 0). Converges to the precision as nu
        increases. (only required if sigma is not specified)

    Examples
    --------
    .. code-block:: python

        with pm.Model():
            x = pm.StudentT('x', nu=15, mu=0, sigma=10)

        with pm.Model():
            x = pm.StudentT('x', nu=15, mu=0, lam=1/23)
    """

    def __init__(self, nu, mu=0, lam=None, sigma=None, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if sd is not None:
            sigma = sd
        self.nu = nu = at.as_tensor_variable(floatX(nu))
        lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
        self.lam = lam = at.as_tensor_variable(lam)
        self.sigma = self.sd = sigma = at.as_tensor_variable(sigma)
        self.mean = self.median = self.mode = self.mu = mu = at.as_tensor_variable(mu)

        self.variance = at.switch((nu > 2) * 1, (1 / self.lam) * (nu / (nu - 2)), np.inf)

        assert_negative_support(lam, "lam (sigma)", "StudentT")
        assert_negative_support(nu, "nu", "StudentT")

    def random(self, point=None, size=None):
        """
        Draw random values from StudentT distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # nu, mu, lam = draw_values([self.nu, self.mu, self.lam], point=point, size=size)
        # return generate_samples(
        #     stats.t.rvs, nu, loc=mu, scale=lam ** -0.5, dist_shape=self.shape, size=size
        # )

    def logp(self, value):
        """
        Calculate log-probability of StudentT distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        nu = self.nu
        mu = self.mu
        lam = self.lam
        sigma = self.sigma

        return bound(
            gammaln((nu + 1.0) / 2.0)
            + 0.5 * at.log(lam / (nu * np.pi))
            - gammaln(nu / 2.0)
            - (nu + 1.0) / 2.0 * at.log1p(lam * (value - mu) ** 2 / nu),
            lam > 0,
            nu > 0,
            sigma > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["nu", "mu", "lam"]

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Student's T distribution
        at the specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log CDF is calculated.

        Returns
        -------
        TensorVariable
        """
        # incomplete_beta function can only handle scalar values (see #4342)
        if np.ndim(value):
            raise TypeError(
                f"StudentT.logcdf expects a scalar value but received a {np.ndim(value)}-dimensional object."
            )

        nu = self.nu
        mu = self.mu
        sigma = self.sigma
        lam = self.lam
        t = (value - mu) / sigma
        sqrt_t2_nu = at.sqrt(t ** 2 + nu)
        x = (t + sqrt_t2_nu) / (2.0 * sqrt_t2_nu)

        return bound(
            at.log(incomplete_beta(nu / 2.0, nu / 2.0, x)),
            0 < nu,
            0 < sigma,
            0 < lam,
        )


class Pareto(Continuous):
    r"""
    Pareto log-likelihood.

    Often used to characterize wealth distribution, or other examples of the
    80/20 rule.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, m) = \frac{\alpha m^{\alpha}}{x^{\alpha+1}}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 4, 1000)
        alphas = [1., 2., 5., 5.]
        ms = [1., 1., 1., 2.]
        for alpha, m in zip(alphas, ms):
            pdf = st.pareto.pdf(x, alpha, scale=m)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, m = {}'.format(alpha, m))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  =============================================================
    Support   :math:`x \in [m, \infty)`
    Mean      :math:`\dfrac{\alpha m}{\alpha - 1}` for :math:`\alpha \ge 1`
    Variance  :math:`\dfrac{m \alpha}{(\alpha - 1)^2 (\alpha - 2)}`
              for :math:`\alpha > 2`
    ========  =============================================================

    Parameters
    ----------
    alpha: float
        Shape parameter (alpha > 0).
    m: float
        Scale parameter (m > 0).
    """
    rv_op = pareto

    @classmethod
    def dist(
        cls, alpha: float = None, m: float = None, no_assert: bool = False, **kwargs
    ) -> RandomVariable:
        alpha = at.as_tensor_variable(floatX(alpha))
        m = at.as_tensor_variable(floatX(m))

        assert_negative_support(alpha, "alpha", "Pareto")
        assert_negative_support(m, "m", "Pareto")

        return super().dist([alpha, m], **kwargs)

    def _random(self, alpha, m, size=None):
        u = np.random.uniform(size=size)
        return m * (1.0 - u) ** (-1.0 / alpha)

    def random(self, point=None, size=None):
        """
        Draw random values from Pareto distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # alpha, m = draw_values([self.alpha, self.m], point=point, size=size)
        # return generate_samples(self._random, alpha, m, dist_shape=self.shape, size=size)

    def logp(
        value: Union[float, np.ndarray, TensorVariable],
        alpha: Union[float, np.ndarray, TensorVariable],
        m: Union[float, np.ndarray, TensorVariable],
    ):
        """
        Calculate log-probability of Pareto distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return bound(
            at.log(alpha) + logpow(m, alpha) - logpow(value, alpha + 1),
            value >= m,
            alpha > 0,
            m > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["alpha", "m"]

    def logcdf(
        value: Union[float, np.ndarray, TensorVariable],
        alpha: Union[float, np.ndarray, TensorVariable],
        m: Union[float, np.ndarray, TensorVariable],
    ):
        """
        Compute the log of the cumulative distribution function for Pareto distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        arg = (m / value) ** alpha
        return bound(
            at.switch(
                at.le(arg, 1e-5),
                at.log1p(-arg),
                at.log(1 - arg),
            ),
            m <= value,
            0 < alpha,
            0 < m,
        )


class Cauchy(Continuous):
    r"""
    Cauchy log-likelihood.

    Also known as the Lorentz or the Breit-Wigner distribution.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, \beta) =
           \frac{1}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-5, 5, 500)
        alphas = [0., 0., 0., -2.]
        betas = [.5, 1., 2., 1.]
        for a, b in zip(alphas, betas):
            pdf = st.cauchy.pdf(x, loc=a, scale=b)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in \mathbb{R}`
    Mode      :math:`\alpha`
    Mean      undefined
    Variance  undefined
    ========  ========================

    Parameters
    ----------
    alpha: float
        Location parameter
    beta: float
        Scale parameter > 0
    """
    rv_op = cauchy

    @classmethod
    def dist(cls, alpha, beta, *args, **kwargs):
        alpha = at.as_tensor_variable(floatX(alpha))
        beta = at.as_tensor_variable(floatX(beta))

        # median = alpha
        # mode = alpha

        assert_negative_support(beta, "beta", "Cauchy")
        return super().dist([alpha, beta], **kwargs)

    def logp(value, alpha, beta):
        """
        Calculate log-probability of Cauchy distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return bound(
            -at.log(np.pi) - at.log(beta) - at.log1p(((value - alpha) / beta) ** 2), beta > 0
        )

    def logcdf(value, alpha, beta):
        """
        Compute the log of the cumulative distribution function for Cauchy distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        return bound(
            at.log(0.5 + at.arctan((value - alpha) / beta) / np.pi),
            0 < beta,
        )


class HalfCauchy(PositiveContinuous):
    r"""
    Half-Cauchy log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \beta) = \frac{2}{\pi \beta [1 + (\frac{x}{\beta})^2]}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 5, 200)
        for b in [0.5, 1.0, 2.0]:
            pdf = st.cauchy.pdf(x, scale=b)
            plt.plot(x, pdf, label=r'$\beta$ = {}'.format(b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in [0, \infty)`
    Mode      0
    Mean      undefined
    Variance  undefined
    ========  ========================

    Parameters
    ----------
    beta: float
        Scale parameter (beta > 0).
    """
    rv_op = halfcauchy

    @classmethod
    def dist(cls, beta, *args, **kwargs):
        beta = at.as_tensor_variable(floatX(beta))
        assert_negative_support(beta, "beta", "HalfCauchy")
        return super().dist([0.0, beta], **kwargs)

    def logp(value, loc, beta):
        """
        Calculate log-probability of HalfCauchy distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return bound(
            at.log(2) - at.log(np.pi) - at.log(beta) - at.log1p(((value - loc) / beta) ** 2),
            value >= loc,
            beta > 0,
        )

    def logcdf(value, loc, beta):
        """
        Compute the log of the cumulative distribution function for HalfCauchy distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        return bound(
            at.log(2 * at.arctan((value - loc) / beta) / np.pi),
            loc <= value,
            0 < beta,
        )


class Gamma(PositiveContinuous):
    r"""
    Gamma log-likelihood.

    Represents the sum of alpha exponentially distributed random variables,
    each of which has mean beta.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, \beta) =
           \frac{\beta^{\alpha}x^{\alpha-1}e^{-\beta x}}{\Gamma(\alpha)}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 20, 200)
        alphas = [1., 2., 3., 7.5]
        betas = [.5, .5, 1., 1.]
        for a, b in zip(alphas, betas):
            pdf = st.gamma.pdf(x, a, scale=1.0/b)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ===============================
    Support   :math:`x \in (0, \infty)`
    Mean      :math:`\dfrac{\alpha}{\beta}`
    Variance  :math:`\dfrac{\alpha}{\beta^2}`
    ========  ===============================

    Gamma distribution can be parameterized either in terms of alpha and
    beta or mean and standard deviation. The link between the two
    parametrizations is given by

    .. math::

       \alpha &= \frac{\mu^2}{\sigma^2} \\
       \beta &= \frac{\mu}{\sigma^2}

    Parameters
    ----------
    alpha: float
        Shape parameter (alpha > 0).
    beta: float
        Rate parameter (beta > 0).
    mu: float
        Alternative shape parameter (mu > 0).
    sigma: float
        Alternative scale parameter (sigma > 0).
    """
    rv_op = gamma

    @classmethod
    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, no_assert=False, **kwargs):
        if sd is not None:
            sigma = sd

        alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
        alpha = at.as_tensor_variable(floatX(alpha))
        beta = at.as_tensor_variable(floatX(beta))

        if not no_assert:
            assert_negative_support(alpha, "alpha", "Gamma")
            assert_negative_support(beta, "beta", "Gamma")

        # The Aesara `GammaRV` `Op` will invert the `beta` parameter itself
        return super().dist([alpha, beta], **kwargs)

    @classmethod
    def get_alpha_beta(cls, alpha=None, beta=None, mu=None, sigma=None):
        if (alpha is not None) and (beta is not None):
            pass
        elif (mu is not None) and (sigma is not None):
            alpha = mu ** 2 / sigma ** 2
            beta = mu / sigma ** 2
        else:
            raise ValueError(
                "Incompatible parameterization. Either use "
                "alpha and beta, or mu and sigma to specify "
                "distribution."
            )

        return alpha, beta

    def logp(value, alpha, inv_beta):
        """
        Calculate log-probability of Gamma distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log
            probabilities for multiple values are desired the values must be
            provided in a numpy array or `TensorVariable`.

        Returns
        -------
        TensorVariable
        """
        beta = at.inv(inv_beta)
        return bound(
            -gammaln(alpha) + logpow(beta, alpha) - beta * value + logpow(value, alpha - 1),
            value >= 0,
            alpha > 0,
            beta > 0,
        )

    def logcdf(value, alpha, inv_beta):
        """
        Compute the log of the cumulative distribution function for Gamma distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or `TensorVariable`
            Value(s) for which log CDF is calculated. If the log CDF for
            multiple values are desired the values must be provided in a numpy
            array or `TensorVariable`.

        Returns
        -------
        TensorVariable
        """
        beta = at.inv(inv_beta)

        # Avoid C-assertion when the gammainc function is called with invalid values (#4340)
        safe_alpha = at.switch(at.lt(alpha, 0), 0, alpha)
        safe_beta = at.switch(at.lt(beta, 0), 0, beta)
        safe_value = at.switch(at.lt(value, 0), 0, value)

        return bound(
            at.log(at.gammainc(safe_alpha, safe_beta * safe_value)),
            0 <= value,
            0 < alpha,
            0 < beta,
        )


class InverseGamma(PositiveContinuous):
    r"""
    Inverse gamma log-likelihood, the reciprocal of the gamma distribution.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, \beta) =
           \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{-\alpha - 1}
           \exp\left(\frac{-\beta}{x}\right)

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 3, 500)
        alphas = [1., 2., 3., 3.]
        betas = [1., 1., 1., .5]
        for a, b in zip(alphas, betas):
            pdf = st.invgamma.pdf(x, a, scale=b)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ======================================================
    Support   :math:`x \in (0, \infty)`
    Mean      :math:`\dfrac{\beta}{\alpha-1}` for :math:`\alpha > 1`
    Variance  :math:`\dfrac{\beta^2}{(\alpha-1)^2(\alpha - 2)}`
              for :math:`\alpha > 2`
    ========  ======================================================

    Parameters
    ----------
    alpha: float
        Shape parameter (alpha > 0).
    beta: float
        Scale parameter (beta > 0).
    mu: float
        Alternative shape parameter (mu > 0).
    sigma: float
        Alternative scale parameter (sigma > 0).
    """
    rv_op = invgamma

    @classmethod
    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwargs):
        if sd is not None:
            sigma = sd

        alpha, beta = cls._get_alpha_beta(alpha, beta, mu, sigma)
        alpha = at.as_tensor_variable(floatX(alpha))
        beta = at.as_tensor_variable(floatX(beta))

        # m = beta / (alpha - 1.0)
        # try:
        #     mean = (alpha > 1) * m or np.inf
        # except ValueError:  # alpha is an array
        #     m[alpha <= 1] = np.inf
        #     mean = m

        # mode = beta / (alpha + 1.0)
        # variance = at.switch(
        #     at.gt(alpha, 2), (beta ** 2) / ((alpha - 2) * (alpha - 1.0) ** 2), np.inf
        # )

        assert_negative_support(alpha, "alpha", "InverseGamma")
        assert_negative_support(beta, "beta", "InverseGamma")

        return super().dist([alpha, beta], **kwargs)

    @classmethod
    def _get_alpha_beta(cls, alpha, beta, mu, sigma):
        if alpha is not None:
            if beta is not None:
                pass
            else:
                beta = 1
        elif (mu is not None) and (sigma is not None):
            alpha = (2 * sigma ** 2 + mu ** 2) / sigma ** 2
            beta = mu * (mu ** 2 + sigma ** 2) / sigma ** 2
        else:
            raise ValueError(
                "Incompatible parameterization. Either use "
                "alpha and (optionally) beta, or mu and sigma to specify "
                "distribution."
            )

        return alpha, beta

    @classmethod
    def _distr_parameters_for_repr(self):
        return ["alpha", "beta"]

    def logp(value, alpha, beta):
        """
        Calculate log-probability of InverseGamma distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return bound(
            logpow(beta, alpha) - gammaln(alpha) - beta / value + logpow(value, -alpha - 1),
            value > 0,
            alpha > 0,
            beta > 0,
        )

    def logcdf(value, alpha, beta):
        """
        Compute the log of the cumulative distribution function for Inverse Gamma distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        # Avoid C-assertion when the gammaincc function is called with invalid values (#4340)
        safe_alpha = at.switch(at.lt(alpha, 0), 0, alpha)
        safe_beta = at.switch(at.lt(beta, 0), 0, beta)
        safe_value = at.switch(at.lt(value, 0), 0, value)

        return bound(
            at.log(at.gammaincc(safe_alpha, safe_beta / safe_value)),
            0 <= value,
            0 < alpha,
            0 < beta,
        )


class ChiSquared(Gamma):
    r"""
    :math:`\chi^2` log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \nu) = \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 15, 200)
        for df in [1, 2, 3, 6, 9]:
            pdf = st.chi2.pdf(x, df)
            plt.plot(x, pdf, label=r'$\nu$ = {}'.format(df))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.ylim(0, 0.6)
        plt.legend(loc=1)
        plt.show()

    ========  ===============================
    Support   :math:`x \in [0, \infty)`
    Mean      :math:`\nu`
    Variance  :math:`2 \nu`
    ========  ===============================

    Parameters
    ----------
    nu: int
        Degrees of freedom (nu > 0).
    """

    def __init__(self, nu, *args, **kwargs):
        self.nu = nu = at.as_tensor_variable(floatX(nu))
        super().__init__(alpha=nu / 2.0, beta=0.5, *args, **kwargs)


class Weibull(PositiveContinuous):
    r"""
    Weibull log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \alpha, \beta) =
           \frac{\alpha x^{\alpha - 1}
           \exp(-(\frac{x}{\beta})^{\alpha})}{\beta^\alpha}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 3, 200)
        alphas = [.5, 1., 1.5, 5., 5.]
        betas = [1., 1., 1., 1.,  2]
        for a, b in zip(alphas, betas):
            pdf = st.weibull_min.pdf(x, a, scale=b)
            plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.ylim(0, 2.5)
        plt.legend(loc=1)
        plt.show()

    ========  ====================================================
    Support   :math:`x \in [0, \infty)`
    Mean      :math:`\beta \Gamma(1 + \frac{1}{\alpha})`
    Variance  :math:`\beta^2 \Gamma(1 + \frac{2}{\alpha} - \mu^2/\beta^2)`
    ========  ====================================================

    Parameters
    ----------
    alpha: float
        Shape parameter (alpha > 0).
    beta: float
        Scale parameter (beta > 0).
    """
    rv_op = weibull

    @classmethod
    def dist(cls, alpha, beta, *args, **kwargs):
        alpha = at.as_tensor_variable(floatX(alpha))
        beta = at.as_tensor_variable(floatX(beta))

        assert_negative_support(alpha, "alpha", "Weibull")
        assert_negative_support(beta, "beta", "Weibull")

        return super().dist([alpha, beta], **kwargs)

    def logp(
        value: Union[float, np.ndarray, TensorVariable],
        alpha: Union[float, np.ndarray, TensorVariable],
        beta: Union[float, np.ndarray, TensorVariable],
    ) -> RandomVariable:
        """
        Calculate log-probability of Weibull distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """

        return bound(
            at.log(alpha)
            - at.log(beta)
            + (alpha - 1) * at.log(value / beta)
            - (value / beta) ** alpha,
            value >= 0,
            alpha > 0,
            beta > 0,
        )

    def logcdf(
        value: Union[float, np.ndarray, TensorVariable],
        alpha: Union[float, np.ndarray, TensorVariable],
        beta: Union[float, np.ndarray, TensorVariable],
    ):
        r"""
        Compute the log of the cumulative distribution function for Weibull distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """

        a = (value / beta) ** alpha
        return bound(
            log1mexp(a),
            0 <= value,
            0 < alpha,
            0 < beta,
        )


class HalfStudentT(PositiveContinuous):
    r"""
    Half Student's T log-likelihood

    The pdf of this distribution is

    .. math::

        f(x \mid \sigma,\nu) =
            \frac{2\;\Gamma\left(\frac{\nu+1}{2}\right)}
            {\Gamma\left(\frac{\nu}{2}\right)\sqrt{\nu\pi\sigma^2}}
            \left(1+\frac{1}{\nu}\frac{x^2}{\sigma^2}\right)^{-\frac{\nu+1}{2}}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 5, 200)
        sigmas = [1., 1., 2., 1.]
        nus = [.5, 1., 1., 30.]
        for sigma, nu in zip(sigmas, nus):
            pdf = st.t.pdf(x, df=nu, loc=0, scale=sigma)
            plt.plot(x, pdf, label=r'$\sigma$ = {}, $\nu$ = {}'.format(sigma, nu))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in [0, \infty)`
    ========  ========================

    Parameters
    ----------
    nu: float
        Degrees of freedom, also known as normality parameter (nu > 0).
    sigma: float
        Scale parameter (sigma > 0). Converges to the standard deviation as nu
        increases. (only required if lam is not specified)
    lam: float
        Scale parameter (lam > 0). Converges to the precision as nu
        increases. (only required if sigma is not specified)

    Examples
    --------
    .. code-block:: python

        # Only pass in one of lam or sigma, but not both.
        with pm.Model():
            x = pm.HalfStudentT('x', sigma=10, nu=10)

        with pm.Model():
            x = pm.HalfStudentT('x', lam=4, nu=10)
    """

    def __init__(self, nu=1, sigma=None, lam=None, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if sd is not None:
            sigma = sd

        self.mode = at.as_tensor_variable(0)
        lam, sigma = get_tau_sigma(lam, sigma)
        self.median = at.as_tensor_variable(sigma)
        self.sigma = self.sd = at.as_tensor_variable(sigma)
        self.lam = at.as_tensor_variable(lam)
        self.nu = nu = at.as_tensor_variable(floatX(nu))

        assert_negative_support(sigma, "sigma", "HalfStudentT")
        assert_negative_support(lam, "lam", "HalfStudentT")
        assert_negative_support(nu, "nu", "HalfStudentT")

    def random(self, point=None, size=None):
        """
        Draw random values from HalfStudentT distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # nu, sigma = draw_values([self.nu, self.sigma], point=point, size=size)
        # return np.abs(
        #     generate_samples(stats.t.rvs, nu, loc=0, scale=sigma, dist_shape=self.shape, size=size)
        # )

    def logp(self, value):
        """
        Calculate log-probability of HalfStudentT distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        nu = self.nu
        sigma = self.sigma
        lam = self.lam

        return bound(
            at.log(2)
            + gammaln((nu + 1.0) / 2.0)
            - gammaln(nu / 2.0)
            - 0.5 * at.log(nu * np.pi * sigma ** 2)
            - (nu + 1.0) / 2.0 * at.log1p(value ** 2 / (nu * sigma ** 2)),
            sigma > 0,
            lam > 0,
            nu > 0,
            value >= 0,
        )

    def _distr_parameters_for_repr(self):
        return ["nu", "lam"]


class ExGaussian(Continuous):
    r"""
    Exponentially modified Gaussian log-likelihood.

    Results from the convolution of a normal distribution with an exponential
    distribution.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, \sigma, \tau) =
           \frac{1}{\nu}\;
           \exp\left\{\frac{\mu-x}{\nu}+\frac{\sigma^2}{2\nu^2}\right\}
           \Phi\left(\frac{x-\mu}{\sigma}-\frac{\sigma}{\nu}\right)

    where :math:`\Phi` is the cumulative distribution function of the
    standard normal distribution.

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-6, 9, 200)
        mus = [0., -2., 0., -3.]
        sigmas = [1., 1., 3., 1.]
        nus = [1., 1., 1., 4.]
        for mu, sigma, nu in zip(mus, sigmas, nus):
            pdf = st.exponnorm.pdf(x, nu/sigma, loc=mu, scale=sigma)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}, $\nu$ = {}'.format(mu, sigma, nu))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ========================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu + \nu`
    Variance  :math:`\sigma^2 + \nu^2`
    ========  ========================

    Parameters
    ----------
    mu: float
        Mean of the normal distribution.
    sigma: float
        Standard deviation of the normal distribution (sigma > 0).
    nu: float
        Mean of the exponential distribution (nu > 0).

    References
    ----------
    .. [Rigby2005] Rigby R.A. and Stasinopoulos D.M. (2005).
        "Generalized additive models for location, scale and shape"
        Applied Statististics., 54, part 3, pp 507-554.

    .. [Lacouture2008] Lacouture, Y. and Couseanou, D. (2008).
        "How to use MATLAB to fit the ex-Gaussian and other probability
        functions to a distribution of response times".
        Tutorials in Quantitative Methods for Psychology,
        Vol. 4, No. 1, pp 35-45.
    """

    def __init__(self, mu=0.0, sigma=None, nu=None, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if sd is not None:
            sigma = sd

        self.mu = mu = at.as_tensor_variable(floatX(mu))
        self.sigma = self.sd = sigma = at.as_tensor_variable(floatX(sigma))
        self.nu = nu = at.as_tensor_variable(floatX(nu))
        self.mean = mu + nu
        self.variance = (sigma ** 2) + (nu ** 2)

        assert_negative_support(sigma, "sigma", "ExGaussian")
        assert_negative_support(nu, "nu", "ExGaussian")

    def random(self, point=None, size=None):
        """
        Draw random values from ExGaussian distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, sigma, nu = draw_values([self.mu, self.sigma, self.nu], point=point, size=size)
        #
        # def _random(mu, sigma, nu, size=None):
        #     return np.random.normal(mu, sigma, size=size) + np.random.exponential(
        #         scale=nu, size=size
        #     )
        #
        # return generate_samples(_random, mu, sigma, nu, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of ExGaussian distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma
        nu = self.nu

        # Alogithm is adapted from dexGAUS.R from gamlss
        return bound(
            at.switch(
                at.gt(nu, 0.05 * sigma),
                (
                    -at.log(nu)
                    + (mu - value) / nu
                    + 0.5 * (sigma / nu) ** 2
                    + normal_lcdf(mu + (sigma ** 2) / nu, sigma, value)
                ),
                log_normal(value, mean=mu, sigma=sigma),
            ),
            0 < sigma,
            0 < nu,
        )

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for ExGaussian distribution
        at the specified value.

        References
        ----------
        .. [Rigby2005] R.A. Rigby (2005).
           "Generalized additive models for location, scale and shape"
           https://doi.org/10.1111/j.1467-9876.2005.00510.x

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma
        nu = self.nu

        # Alogithm is adapted from pexGAUS.R from gamlss
        return bound(
            at.switch(
                at.gt(nu, 0.05 * sigma),
                logdiffexp(
                    normal_lcdf(mu, sigma, value),
                    (
                        (mu - value) / nu
                        + 0.5 * (sigma / nu) ** 2
                        + normal_lcdf(mu + (sigma ** 2) / nu, sigma, value)
                    ),
                ),
                normal_lcdf(mu, sigma, value),
            ),
            0 < sigma,
            0 < nu,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "sigma", "nu"]


class VonMises(Continuous):
    r"""
    Univariate VonMises log-likelihood.

    The pdf of this distribution is

    .. math::

        f(x \mid \mu, \kappa) =
            \frac{e^{\kappa\cos(x-\mu)}}{2\pi I_0(\kappa)}

    where :math:`I_0` is the modified Bessel function of order 0.

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-np.pi, np.pi, 200)
        mus = [0., 0., 0.,  -2.5]
        kappas = [.01, 0.5,  4., 2.]
        for mu, kappa in zip(mus, kappas):
            pdf = st.vonmises.pdf(x, kappa, loc=mu)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\kappa$ = {}'.format(mu, kappa))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in [-\pi, \pi]`
    Mean      :math:`\mu`
    Variance  :math:`1-\frac{I_1(\kappa)}{I_0(\kappa)}`
    ========  ==========================================

    Parameters
    ----------
    mu: float
        Mean.
    kappa: float
        Concentration (\frac{1}{kappa} is analogous to \sigma^2).
    """

    def __init__(self, mu=0.0, kappa=None, transform="circular", *args, **kwargs):
        if transform == "circular":
            transform = transforms.Circular()
        super().__init__(transform=transform, *args, **kwargs)
        self.mean = self.median = self.mode = self.mu = mu = at.as_tensor_variable(floatX(mu))
        self.kappa = kappa = at.as_tensor_variable(floatX(kappa))

        assert_negative_support(kappa, "kappa", "VonMises")

    def random(self, point=None, size=None):
        """
        Draw random values from VonMises distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, kappa = draw_values([self.mu, self.kappa], point=point, size=size)
        # return generate_samples(
        #     stats.vonmises.rvs, loc=mu, kappa=kappa, dist_shape=self.shape, size=size
        # )

    def logp(self, value):
        """
        Calculate log-probability of VonMises distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        kappa = self.kappa
        return bound(
            kappa * at.cos(mu - value) - (at.log(2 * np.pi) + log_i0(kappa)),
            kappa > 0,
            value >= -np.pi,
            value <= np.pi,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "kappa"]


class SkewNormal(Continuous):
    r"""
    Univariate skew-normal log-likelihood.

     The pdf of this distribution is

    .. math::

       f(x \mid \mu, \tau, \alpha) =
       2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-4, 4, 200)
        for alpha in [-6, 0, 6]:
            pdf = st.skewnorm.pdf(x, alpha, loc=0, scale=1)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}, $\alpha$ = {}'.format(0, 1, alpha))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu + \sigma \sqrt{\frac{2}{\pi}} \frac {\alpha }{{\sqrt {1+\alpha ^{2}}}}`
    Variance  :math:`\sigma^2 \left(  1-\frac{2\alpha^2}{(\alpha^2+1) \pi} \right)`
    ========  ==========================================

    Skew-normal distribution can be parameterized either in terms of precision
    or standard deviation. The link between the two parametrizations is
    given by

    .. math::
       \tau = \dfrac{1}{\sigma^2}

    Parameters
    ----------
    mu: float
        Location parameter.
    sigma: float
        Scale parameter (sigma > 0).
    tau: float
        Alternative scale parameter (tau > 0).
    alpha: float
        Skewness parameter.

    Notes
    -----
    When alpha=0 we recover the Normal distribution and mu becomes the mean,
    tau the precision and sigma the standard deviation. In the limit of alpha
    approaching plus/minus infinite we get a half-normal distribution.

    """

    def __init__(self, mu=0.0, sigma=None, tau=None, alpha=1, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if sd is not None:
            sigma = sd

        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
        self.mu = mu = at.as_tensor_variable(floatX(mu))
        self.tau = at.as_tensor_variable(tau)
        self.sigma = self.sd = at.as_tensor_variable(sigma)

        self.alpha = alpha = at.as_tensor_variable(floatX(alpha))

        self.mean = mu + self.sigma * (2 / np.pi) ** 0.5 * alpha / (1 + alpha ** 2) ** 0.5
        self.variance = self.sigma ** 2 * (1 - (2 * alpha ** 2) / ((1 + alpha ** 2) * np.pi))

        assert_negative_support(tau, "tau", "SkewNormal")
        assert_negative_support(sigma, "sigma", "SkewNormal")

    def random(self, point=None, size=None):
        """
        Draw random values from SkewNormal distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, tau, _, alpha = draw_values(
        #     [self.mu, self.tau, self.sigma, self.alpha], point=point, size=size
        # )
        # return generate_samples(
        #     stats.skewnorm.rvs, a=alpha, loc=mu, scale=tau ** -0.5, dist_shape=self.shape, size=size
        # )

    def logp(self, value):
        """
        Calculate log-probability of SkewNormal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        tau = self.tau
        sigma = self.sigma
        mu = self.mu
        alpha = self.alpha
        return bound(
            at.log(1 + at.erf(((value - mu) * at.sqrt(tau) * alpha) / at.sqrt(2)))
            + (-tau * (value - mu) ** 2 + at.log(tau / np.pi / 2.0)) / 2.0,
            tau > 0,
            sigma > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "sigma", "alpha"]


class Triangular(BoundedContinuous):
    r"""
    Continuous Triangular log-likelihood

    The pdf of this distribution is

    .. math::

       \begin{cases}
         0 & \text{for } x < a, \\
         \frac{2(x-a)}{(b-a)(c-a)} & \text{for } a \le x < c, \\[4pt]
         \frac{2}{b-a}             & \text{for } x = c, \\[4pt]
         \frac{2(b-x)}{(b-a)(b-c)} & \text{for } c < x \le b, \\[4pt]
         0 & \text{for } b < x.
        \end{cases}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-2, 10, 500)
        lowers = [0., -1, 2]
        cs = [2., 0., 6.5]
        uppers = [4., 1, 8]
        for lower, c, upper in zip(lowers, cs, uppers):
            scale = upper - lower
            c_ = (c - lower) / scale
            pdf = st.triang.pdf(x, loc=lower, c=c_, scale=scale)
            plt.plot(x, pdf, label='lower = {}, c = {}, upper = {}'.format(lower,
                                                                           c,
                                                                           upper))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ============================================================================
    Support   :math:`x \in [lower, upper]`
    Mean      :math:`\dfrac{lower + upper + c}{3}`
    Variance  :math:`\dfrac{upper^2 + lower^2 +c^2 - lower*upper - lower*c - upper*c}{18}`
    ========  ============================================================================

    Parameters
    ----------
    lower: float
        Lower limit.
    c: float
        mode
    upper: float
        Upper limit.
    """

    def __init__(self, lower=0, upper=1, c=0.5, *args, **kwargs):
        self.median = self.mean = self.c = c = at.as_tensor_variable(floatX(c))
        self.lower = lower = at.as_tensor_variable(floatX(lower))
        self.upper = upper = at.as_tensor_variable(floatX(upper))

        super().__init__(lower=lower, upper=upper, *args, **kwargs)

    def random(self, point=None, size=None):
        """
        Draw random values from Triangular distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # c, lower, upper = draw_values([self.c, self.lower, self.upper], point=point, size=size)
        # return generate_samples(
        #     self._random, c=c, lower=lower, upper=upper, size=size, dist_shape=self.shape
        # )

    def _random(self, c, lower, upper, size):
        """Wrapper around stats.triang.rvs that converts Triangular's
        parametrization to scipy.triang. All parameter arrays should have
        been broadcasted properly by generate_samples at this point and size is
        the scipy.rvs representation.
        """
        scale = upper - lower
        return stats.triang.rvs(c=(c - lower) / scale, loc=lower, scale=scale, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Triangular distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        c = self.c
        lower = self.lower
        upper = self.upper
        return bound(
            at.switch(
                at.lt(value, c),
                at.log(2 * (value - lower) / ((upper - lower) * (c - lower))),
                at.log(2 * (upper - value) / ((upper - lower) * (upper - c))),
            ),
            lower <= value,
            value <= upper,
        )

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Triangular distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        c = self.c
        lower = self.lower
        upper = self.upper
        return bound(
            at.switch(
                at.le(value, lower),
                -np.inf,
                at.switch(
                    at.le(value, c),
                    at.log(((value - lower) ** 2) / ((upper - lower) * (c - lower))),
                    at.switch(
                        at.lt(value, upper),
                        at.log1p(-((upper - value) ** 2) / ((upper - lower) * (upper - c))),
                        0,
                    ),
                ),
            ),
            lower <= upper,
        )


class Gumbel(Continuous):
    r"""
        Univariate Gumbel log-likelihood

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, \beta) = \frac{1}{\beta}e^{-(z + e^{-z})}

    where

    .. math::

        z = \frac{x - \mu}{\beta}.

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-10, 20, 200)
        mus = [0., 4., -1.]
        betas = [2., 2., 4.]
        for mu, beta in zip(mus, betas):
            pdf = st.gumbel_r.pdf(x, loc=mu, scale=beta)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\beta$ = {}'.format(mu, beta))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()


    ========  ==========================================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu + \beta\gamma`, where :math:`\gamma` is the Euler-Mascheroni constant
    Variance  :math:`\frac{\pi^2}{6} \beta^2`
    ========  ==========================================

    Parameters
    ----------
    mu: float
        Location parameter.
    beta: float
        Scale parameter (beta > 0).
    """
    rv_op = gumbel

    @classmethod
    def dist(
        cls, mu: float = None, beta: float = None, no_assert: bool = False, **kwargs
    ) -> RandomVariable:

        mu = at.as_tensor_variable(floatX(mu))
        beta = at.as_tensor_variable(floatX(beta))

        if not no_assert:
            assert_negative_support(beta, "beta", "Gumbel")

        return super().dist([mu, beta], **kwargs)

    def _distr_parameters_for_repr(self):
        return ["mu", "beta"]

    def logp(
        value: Union[float, np.ndarray, TensorVariable],
        mu: Union[float, np.ndarray, TensorVariable],
        beta: Union[float, np.ndarray, TensorVariable],
    ) -> TensorVariable:
        """
        Calculate log-probability of Gumbel distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        scaled = (value - mu) / beta
        return bound(
            -scaled - at.exp(-scaled) - at.log(beta),
            0 < beta,
        )

    def logcdf(
        value: Union[float, np.ndarray, TensorVariable],
        mu: Union[float, np.ndarray, TensorVariable],
        beta: Union[float, np.ndarray, TensorVariable],
    ) -> TensorVariable:
        """
        Compute the log of the cumulative distribution function for Gumbel distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        return bound(
            -at.exp(-(value - mu) / beta),
            0 < beta,
        )


class Rice(PositiveContinuous):
    r"""
    Rice distribution.

    .. math::

       f(x\mid \nu ,\sigma )=
       {\frac  {x}{\sigma ^{2}}}\exp
       \left({\frac  {-(x^{2}+\nu ^{2})}{2\sigma ^{2}}}\right)I_{0}\left({\frac  {x\nu }{\sigma ^{2}}}\right),

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0, 8, 500)
        nus = [0., 0., 4., 4.]
        sigmas = [1., 2., 1., 2.]
        for nu, sigma in  zip(nus, sigmas):
            pdf = st.rice.pdf(x, nu / sigma, scale=sigma)
            plt.plot(x, pdf, label=r'$\nu$ = {}, $\sigma$ = {}'.format(nu, sigma))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==============================================================
    Support   :math:`x \in (0, \infty)`
    Mean      :math:`\sigma {\sqrt  {\pi /2}}\,\,L_{{1/2}}(-\nu ^{2}/2\sigma ^{2})`
    Variance  :math:`2\sigma ^{2}+\nu ^{2}-{\frac  {\pi \sigma ^{2}}{2}}L_{{1/2}}^{2}\left({\frac  {-\nu ^{2}}{2\sigma ^{2}}}\right)`
    ========  ==============================================================


    Parameters
    ----------
    nu: float
        noncentrality parameter.
    sigma: float
        scale parameter.
    b: float
        shape parameter (alternative to nu).

    Notes
    -----
    The distribution :math:`\mathrm{Rice}\left(|\nu|,\sigma\right)` is the
    distribution of :math:`R=\sqrt{X^2+Y^2}` where :math:`X\sim N(\nu \cos{\theta}, \sigma^2)`,
    :math:`Y\sim N(\nu \sin{\theta}, \sigma^2)` are independent and for any
    real :math:`\theta`.

    The distribution is defined with either nu or b.
    The link between the two parametrizations is given by

    .. math::

       b = \dfrac{\nu}{\sigma}

    """

    def __init__(self, nu=None, sigma=None, b=None, sd=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if sd is not None:
            sigma = sd

        nu, b, sigma = self.get_nu_b(nu, b, sigma)
        self.nu = nu = at.as_tensor_variable(floatX(nu))
        self.sigma = self.sd = sigma = at.as_tensor_variable(floatX(sigma))
        self.b = b = at.as_tensor_variable(floatX(b))

        nu_sigma_ratio = -(nu ** 2) / (2 * sigma ** 2)
        self.mean = (
            sigma
            * np.sqrt(np.pi / 2)
            * at.exp(nu_sigma_ratio / 2)
            * (
                (1 - nu_sigma_ratio) * at.i0(-nu_sigma_ratio / 2)
                - nu_sigma_ratio * at.i1(-nu_sigma_ratio / 2)
            )
        )
        self.variance = (
            2 * sigma ** 2
            + nu ** 2
            - (np.pi * sigma ** 2 / 2)
            * (
                at.exp(nu_sigma_ratio / 2)
                * (
                    (1 - nu_sigma_ratio) * at.i0(-nu_sigma_ratio / 2)
                    - nu_sigma_ratio * at.i1(-nu_sigma_ratio / 2)
                )
            )
            ** 2
        )

    def get_nu_b(self, nu, b, sigma):
        if sigma is None:
            sigma = 1.0
        if nu is None and b is not None:
            nu = b * sigma
            return nu, b, sigma
        elif nu is not None and b is None:
            b = nu / sigma
            return nu, b, sigma
        raise ValueError("Rice distribution must specify either nu" " or b.")

    def random(self, point=None, size=None):
        """
        Draw random values from Rice distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # nu, sigma = draw_values([self.nu, self.sigma], point=point, size=size)
        # return generate_samples(self._random, nu=nu, sigma=sigma, dist_shape=self.shape, size=size)

    def _random(self, nu, sigma, size):
        """Wrapper around stats.rice.rvs that converts Rice's
        parametrization to scipy.rice. All parameter arrays should have
        been broadcasted properly by generate_samples at this point and size is
        the scipy.rvs representation.
        """
        return stats.rice.rvs(b=nu / sigma, scale=sigma, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Rice distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        nu = self.nu
        sigma = self.sigma
        b = self.b
        x = value / sigma
        return bound(
            at.log(x * at.exp((-(x - b) * (x - b)) / 2) * i0e(x * b) / sigma),
            sigma >= 0,
            nu >= 0,
            value > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["nu", "sigma"]


class Logistic(Continuous):
    r"""
    Logistic log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu, s) =
           \frac{\exp\left(-\frac{x - \mu}{s}\right)}{s \left(1 + \exp\left(-\frac{x - \mu}{s}\right)\right)^2}

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-5, 5, 200)
        mus = [0., 0., 0., -2.]
        ss = [.4, 1., 2., .4]
        for mu, s in zip(mus, ss):
            pdf = st.logistic.pdf(x, loc=mu, scale=s)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $s$ = {}'.format(mu, s))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in \mathbb{R}`
    Mean      :math:`\mu`
    Variance  :math:`\frac{s^2 \pi^2}{3}`
    ========  ==========================================


    Parameters
    ----------
    mu: float
        Mean.
    s: float
        Scale (s > 0).
    """

    def __init__(self, mu=0.0, s=1.0, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.mu = at.as_tensor_variable(floatX(mu))
        self.s = at.as_tensor_variable(floatX(s))

        self.mean = self.mode = mu
        self.variance = s ** 2 * np.pi ** 2 / 3.0

    def random(self, point=None, size=None):
        """
        Draw random values from Logistic distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, s = draw_values([self.mu, self.s], point=point, size=size)
        #
        # return generate_samples(
        #     stats.logistic.rvs, loc=mu, scale=s, dist_shape=self.shape, size=size
        # )

    def logp(self, value):
        """
        Calculate log-probability of Logistic distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        s = self.s

        return bound(
            -(value - mu) / s - at.log(s) - 2 * at.log1p(at.exp(-(value - mu) / s)),
            s > 0,
        )

    def logcdf(self, value):
        r"""
        Compute the log of the cumulative distribution function for Logistic distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        s = self.s
        return bound(
            -log1pexp(-(value - mu) / s),
            0 < s,
        )


class LogitNormal(UnitContinuous):
    r"""
    Logit-Normal log-likelihood.

    The pdf of this distribution is

    .. math::
       f(x \mid \mu, \tau) =
           \frac{1}{x(1-x)} \sqrt{\frac{\tau}{2\pi}}
           \exp\left\{ -\frac{\tau}{2} (logit(x)-\mu)^2 \right\}


    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        from scipy.special import logit
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(0.0001, 0.9999, 500)
        mus = [0., 0., 0., 1.]
        sigmas = [0.3, 1., 2., 1.]
        for mu, sigma in  zip(mus, sigmas):
            pdf = st.norm.pdf(logit(x), loc=mu, scale=sigma) * 1/(x * (1-x))
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}'.format(mu, sigma))
            plt.legend(loc=1)
        plt.show()

    ========  ==========================================
    Support   :math:`x \in (0, 1)`
    Mean      no analytical solution
    Variance  no analytical solution
    ========  ==========================================

    Parameters
    ----------
    mu: float
        Location parameter.
    sigma: float
        Scale parameter (sigma > 0).
    tau: float
        Scale parameter (tau > 0).
    """

    def __init__(self, mu=0, sigma=None, tau=None, sd=None, **kwargs):
        if sd is not None:
            sigma = sd
        self.mu = mu = at.as_tensor_variable(floatX(mu))
        tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
        self.sigma = self.sd = at.as_tensor_variable(sigma)
        self.tau = tau = at.as_tensor_variable(tau)

        self.median = invlogit(mu)
        assert_negative_support(sigma, "sigma", "LogitNormal")
        assert_negative_support(tau, "tau", "LogitNormal")

        super().__init__(**kwargs)

    def random(self, point=None, size=None):
        """
        Draw random values from LogitNormal distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, _, sigma = draw_values([self.mu, self.tau, self.sigma], point=point, size=size)
        # return expit(
        #     generate_samples(stats.norm.rvs, loc=mu, scale=sigma, dist_shape=self.shape, size=size)
        # )

    def logp(self, value):
        """
        Calculate log-probability of LogitNormal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        tau = self.tau
        return bound(
            -0.5 * tau * (logit(value) - mu) ** 2
            + 0.5 * at.log(tau / (2.0 * np.pi))
            - at.log(value * (1 - value)),
            value > 0,
            value < 1,
            tau > 0,
        )

    def _distr_parameters_for_repr(self):
        return ["mu", "sigma"]


class Interpolated(BoundedContinuous):
    r"""
    Univariate probability distribution defined as a linear interpolation
    of probability density function evaluated on some lattice of points.

    The lattice can be uneven, so the steps between different points can have
    different size and it is possible to vary the precision between regions
    of the support.

    The probability density function values don not have to be normalized, as the
    interpolated density is any way normalized to make the total probability
    equal to $1$.

    Both parameters ``x_points`` and values ``pdf_points`` are not variables, but
    plain array-like objects, so they are constant and cannot be sampled.

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import pymc3 as pm
        import arviz as az
        from scipy.stats import gamma
        plt.style.use('arviz-darkgrid')
        rv = gamma(1.99)
        x = np.linspace(rv.ppf(0.01),rv.ppf(0.99), 1000)
        points = np.linspace(x[0], x[-1], 50)
        pdf = rv.pdf(points)
        interpolated = pm.Interpolated.dist(points, pdf)
        fig, ax = plt.subplots(1, 1)
        ax.plot(x, rv.pdf(x), 'C0', linestyle = '--',  label='Original Gamma pdf',alpha=0.8,lw=2)
        ax.plot(points, pdf, color='black', marker='o', label='Lattice Points',alpha=0.5,linestyle='')
        ax.plot(x, np.exp(interpolated.logp(x).eval()),'C1',label='Interpolated pdf',alpha=0.8,lw=3)
        r = interpolated.random(size=1000)
        ax.hist(r, density=True, alpha=0.4,align ='mid',color='grey')
        ax.legend(loc='best', frameon=False)
        plt.show()

    ========  ===========================================
    Support   :math:`x \in [x\_points[0], x\_points[-1]]`
    ========  ===========================================

    Parameters
    ----------
    x_points: array-like
        A monotonically growing list of values
    pdf_points: array-like
        Probability density function evaluated on lattice ``x_points``
    """

    def __init__(self, x_points, pdf_points, *args, **kwargs):
        self.lower = lower = at.as_tensor_variable(x_points[0])
        self.upper = upper = at.as_tensor_variable(x_points[-1])

        super().__init__(lower=lower, upper=upper, *args, **kwargs)

        interp = InterpolatedUnivariateSpline(x_points, pdf_points, k=1, ext="zeros")
        Z = interp.integral(x_points[0], x_points[-1])

        self.Z = at.as_tensor_variable(Z)
        self.interp_op = SplineWrapper(interp)
        self.x_points = x_points
        self.pdf_points = pdf_points / Z
        self.cdf_points = interp.antiderivative()(x_points) / Z

        self.median = self._argcdf(0.5)

    def _argcdf(self, p):
        pdf = self.pdf_points
        cdf = self.cdf_points
        x = self.x_points

        index = np.searchsorted(cdf, p) - 1
        slope = (pdf[index + 1] - pdf[index]) / (x[index + 1] - x[index])

        return x[index] + np.where(
            np.abs(slope) <= 1e-8,
            np.where(
                np.abs(pdf[index]) <= 1e-8, np.zeros(index.shape), (p - cdf[index]) / pdf[index]
            ),
            (-pdf[index] + np.sqrt(pdf[index] ** 2 + 2 * slope * (p - cdf[index]))) / slope,
        )

    def _random(self, size=None):
        return self._argcdf(np.random.uniform(size=size))

    def random(self, point=None, size=None):
        """
        Draw random values from Interpolated distribution.

        Parameters
        ----------
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # return generate_samples(self._random, dist_shape=self.shape, size=size)

    def logp(self, value):
        """
        Calculate log-probability of Interpolated distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        return at.log(self.interp_op(value) / self.Z)

    def _distr_parameters_for_repr(self):
        return []


class Moyal(Continuous):
    r"""
    Moyal log-likelihood.

    The pdf of this distribution is

    .. math::

       f(x \mid \mu,\sigma) = \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{1}{2}\left(z + e^{-z}\right)},

    where

    .. math::

       z = \frac{x-\mu}{\sigma}.

    .. plot::

        import matplotlib.pyplot as plt
        import numpy as np
        import scipy.stats as st
        import arviz as az
        plt.style.use('arviz-darkgrid')
        x = np.linspace(-10, 20, 200)
        mus = [-1., 0., 4.]
        sigmas = [2., 2., 4.]
        for mu, sigma in zip(mus, sigmas):
            pdf = st.moyal.pdf(x, loc=mu, scale=sigma)
            plt.plot(x, pdf, label=r'$\mu$ = {}, $\sigma$ = {}'.format(mu, sigma))
        plt.xlabel('x', fontsize=12)
        plt.ylabel('f(x)', fontsize=12)
        plt.legend(loc=1)
        plt.show()

    ========  ==============================================================
    Support   :math:`x \in (-\infty, \infty)`
    Mean      :math:`\mu + \sigma\left(\gamma + \log 2\right)`, where :math:`\gamma` is the Euler-Mascheroni constant
    Variance  :math:`\frac{\pi^{2}}{2}\sigma^{2}`
    ========  ==============================================================

    Parameters
    ----------
    mu: float
        Location parameter.
    sigma: float
        Scale parameter (sigma > 0).
    """

    def __init__(self, mu=0, sigma=1.0, *args, **kwargs):
        self.mu = at.as_tensor_variable(floatX(mu))
        self.sigma = at.as_tensor_variable(floatX(sigma))

        assert_negative_support(sigma, "sigma", "Moyal")

        self.mean = self.mu + self.sigma * (np.euler_gamma + at.log(2))
        self.median = self.mu - self.sigma * at.log(2 * at.erfcinv(1 / 2) ** 2)
        self.mode = self.mu
        self.variance = (np.pi ** 2 / 2.0) * self.sigma ** 2

        super().__init__(*args, **kwargs)

    def random(self, point=None, size=None):
        """
        Draw random values from Moyal distribution.

        Parameters
        ----------
        point: dict, optional
            Dict of variable values on which random values are to be
            conditioned (uses default point if not specified).
        size: int, optional
            Desired size of random sample (returns one sample if not
            specified).

        Returns
        -------
        array
        """
        # mu, sigma = draw_values([self.mu, self.sigma], point=point, size=size)
        # return generate_samples(
        #     stats.moyal.rvs, loc=mu, scale=sigma, dist_shape=self.shape, size=size
        # )

    def logp(self, value):
        """
        Calculate log-probability of Moyal distribution at specified value.

        Parameters
        ----------
        value: numeric
            Value(s) for which log-probability is calculated. If the log probabilities for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma
        scaled = (value - mu) / sigma
        return bound(
            (-(1 / 2) * (scaled + at.exp(-scaled)) - at.log(sigma) - (1 / 2) * at.log(2 * np.pi)),
            0 < sigma,
        )

    def logcdf(self, value):
        """
        Compute the log of the cumulative distribution function for Moyal distribution
        at the specified value.

        Parameters
        ----------
        value: numeric or np.ndarray or aesara.tensor
            Value(s) for which log CDF is calculated. If the log CDF for multiple
            values are desired the values must be provided in a numpy array or Aesara tensor.

        Returns
        -------
        TensorVariable
        """
        mu = self.mu
        sigma = self.sigma

        scaled = (value - mu) / sigma
        return bound(
            at.log(at.erfc(at.exp(-scaled / 2) * (2 ** -0.5))),
            0 < sigma,
        )