Add release date, remove fixes category.

Author: twiecki

RELEASE-NOTES.md

@@ -1,6 +1,6 @@
 # Release Notes
 
-## PyMC3 3.7 (unreleased)
+## PyMC3 3.7 (May 29 2019)
 
 ### New features
 
@@ -11,13 +11,10 @@
 - Added the `distributions.shape_utils` module with functions used to help broadcast samples drawn from distributions using the `size` keyword argument.
 - Used `numpy.vectorize` in `distributions.distribution._compile_theano_function`. This enables `sample_prior_predictive` and `sample_posterior_predictive` to ask for tuples of samples instead of just integers. This fixes issue [#3422](https://github.com/pymc-devs/pymc3/issues/3422).
 
-### Fixes
-
-- `HamiltonianMC` was ignoring certain arguments like `target_accept`, and not using the custom step size jitter function with expectation 1.
-
 ### Maintenance
 
 - All occurances of `sd` as a parameter name have been renamed to `sigma`. `sd` will continue to function for backwards compatibility.
+- `HamiltonianMC` was ignoring certain arguments like `target_accept`, and not using the custom step size jitter function with expectation 1.
 - Made `BrokenPipeError` for parallel sampling more verbose on Windows.
 - Added the `broadcast_distribution_samples` function that helps broadcasting arrays of drawn samples, taking into account the requested `size` and the inferred distribution shape. This sometimes is needed by distributions that call several `rvs` separately within their `random` method, such as the `ZeroInflatedPoisson` (fixes issue [#3310](https://github.com/pymc-devs/pymc3/issues/3310)).
 - The `Wald`, `Kumaraswamy`, `LogNormal`, `Pareto`, `Cauchy`, `HalfCauchy`, `Weibull` and `ExGaussian` distributions `random` method used a hidden `_random` function that was written with scalars in mind. This could potentially lead to artificial correlations between random draws. Added shape guards and broadcasting of the distribution samples to prevent this (Similar to issue [#3310](https://github.com/pymc-devs/pymc3/issues/3310)).