Implement backwards-compatble shape and Ellipsis-enabled dims

Co-authored-by: Thomas Wiecki <[email protected]>
Co-authored-by: Ricardo <[email protected]>

Author: 3 people

RELEASE-NOTES.md

@@ -10,6 +10,10 @@
 
 ### New Features
 - The `CAR` distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.
+- The dimensionality of model variables can now be parametrized through either of `shape`, `dims` or `size` (see [#4696](https://github.com/pymc-devs/pymc3/pull/4696)):
+  - With `shape` the length of dimensions must be given numerically or as scalar Aesara `Variables`. Numeric entries in `shape` restrict the model variable to the exact length and re-sizing is no longer possible.
+  - `dims` keeps model variables re-sizeable (for example through `pm.Data`) and leads to well defined coordinates in `InferenceData` objects. An `Ellipsis` (`...`) in the last position of `dims` can be used as short-hand notation for implied dimensions.
+  - The `size` kwarg behaves like it does in Aesara/NumPy. For univariate RVs it is the same as `shape`, but for multivariate RVs it depends on how the RV implements broadcasting to dimensionality greater than `RVOp.ndim_supp`.
 - Add `logcdf` method to Kumaraswamy distribution (see [#4706](https://github.com/pymc-devs/pymc3/pull/4706)).
 - ...