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Merge pull request #2223 from pymc-devs/bump_v3.1rc4
Bump version to 3.1rc4
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CHANGELOG.md

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RELEASE-NOTES.md

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# Release Notes
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## PyMC3 3.1 (TBA)
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## PyMC3 3.1 (June 23, 2017)
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### New features
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* [Add Gaussian Process submodule](http://pymc-devs.github.io/pymc3/notebooks/GP-introduction.html)
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* New user forum at http://discourse.pymc.io
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* [Gaussian Process submodule](http://pymc-devs.github.io/pymc3/notebooks/GP-introduction.html)
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* Much improved variational inference support:
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- [Add Operator Variational Inference (experimental).](http://pymc-devs.github.io/pymc3/notebooks/bayesian_neural_network_opvi-advi.html)
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- [Add Stein-Variational Gradient Descent as well as Amortized SVGD (experimental).](https://github.com/pymc-devs/pymc3/pull/2183)
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- [Add pm.Minibatch() to easily specify mini-batches.](http://pymc-devs.github.io/pymc3/notebooks/bayesian_neural_network_opvi-advi.html#Minibatch-ADVI)
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- Added various optimizers including ADAM.
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- Stopping criterion implemented via callbacks.
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* sample() defaults changed: tuning is enabled for the first 500 samples which are then discarded from the trace as burn-in.
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* MvNormal supports Cholesky Decomposition now for increased speed and numerical stability.
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* Many optimizations and speed-ups.
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* NUTS implementation now matches current Stan implementation.
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* Much improved variational inference support:
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- [Add Operator Variational Inference (experimental).](http://pymc-devs.github.io/pymc3/notebooks/bayesian_neural_network_opvi-advi.html)
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- [Added Operator Variational Inference (experimental).](http://pymc-devs.github.io/pymc3/notebooks/variational_api_quickstart.html)
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- [Add Stein-Variational Gradient Descent as well as Amortized SVGD (experimental).](https://github.com/pymc-devs/pymc3/pull/2183)
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- [Added Stein-Variational Gradient Descent as well as Amortized SVGD (experimental).](https://github.com/pymc-devs/pymc3/pull/2183)
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- [Add pm.generator() to easily specify mini-batches.](http://pymc-devs.github.io/pymc3/notebooks/bayesian_neural_network_opvi-advi.html#Minibatch-ADVI)
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- [Added `Minibatch` to easily specify mini-batches.](http://pymc-devs.github.io/pymc3/notebooks/bayesian_neural_network_opvi-advi.html#Minibatch-ADVI)
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- Added full-rank ADVI
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- Added various optimizers including ADAM.
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- Deprecated old ADVI interface
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- implemented `fit` function as the primary interface to approximation algorithms
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* Added support for multidimensional minibatches
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* [Sampled posteriors can now be turned into priors for Bayesian updating with a new interpolated distribution.](https://github.com/pymc-devs/pymc3/pull/2163)
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* Added `Approximation` class and the ability to convert a sampled trace into an approximation via its `Empirical` subclass.
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* `Model` can now be inherited from and act as a base class for user specified models (see pymc3.models.linear).
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* Add MvGaussianRandomWalk and MvStudentTRandomWalk distributions.
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* GLM models do not need a left-hand variable anymore.
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* Add support for cholesky parametrizations for cov/corr matrices.
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* Refactored HMC and NUTS for better readability.
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* Add support for Python 3.6.

docs/source/conf.py

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# The short X.Y version.
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version = '3.1'
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# The full version, including alpha/beta/rc tags.
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release = '3.1rc3'
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release = '3.1rc4'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.

docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb

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docs/source/notebooks/GLM-hierarchical-advi-minibatch.ipynb

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docs/source/notebooks/GP-introduction.ipynb

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docs/source/notebooks/GP-slice-sampling.ipynb

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docs/source/notebooks/LKJ.ipynb

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docs/source/notebooks/Model Comparison.ipynb

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docs/source/notebooks/PyMC3_tips_and_heuristic.ipynb

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docs/source/notebooks/SMC2_gaussians.ipynb

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docs/source/notebooks/bayesian_neural_network_advi.ipynb

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docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb

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docs/source/notebooks/cox_model.ipynb

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docs/source/notebooks/gaussian_process.ipynb

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docs/source/notebooks/getting_started.ipynb

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docs/source/notebooks/lda-advi-aevb.ipynb

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pymc3/__init__.py

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# pylint: disable=wildcard-import
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__version__ = "3.1rc3"
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__version__ = "3.1rc4"
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from .blocking import *
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from .distributions import *

pymc3/distributions/mixture.py

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return np.random.choice(k, p=w, *args, **kwargs)
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w = draw_values([self.w], point=point)
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w = draw_values([self.w], point=point)[0]
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w_samples = generate_samples(random_choice,
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w=w,

setup.py

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AUTHOR_EMAIL = '[email protected]'
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URL = "http://github.com/pymc-devs/pymc3"
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LICENSE = "Apache License, Version 2.0"
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VERSION = "3.1rc3"
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VERSION = "3.1rc4"
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classifiers = ['Development Status :: 5 - Production/Stable',
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'Programming Language :: Python',

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