Updates to release notes

Author: fonnesbeck

RELEASE-NOTES.md

@@ -1,12 +1,12 @@
 # Release Notes
 
-## PyMC3 3.2 (October 2, 2017)
+## PyMC3 3.2 (October 10, 2017)
 
 ### New features
 
 This version includes two major contributions from our Google Summer of Code 2017 students:
 
-* Maxim Kucherov extended and refactored the variational inference module. This primarily adds two important classes, representing operator variational inference (`OPVI`) objects and `Approximation` objects. These make it easier to extend existing `variational` classes, and to derive inference from `variational` optimizations, respectively. The `variational` module now also includes normalizing flows (`NFVI`).
+* Maxim Kochurov extended and refactored the variational inference module. This primarily adds two important classes, representing operator variational inference (`OPVI`) objects and `Approximation` objects. These make it easier to extend existing `variational` classes, and to derive inference from `variational` optimizations, respectively. The `variational` module now also includes normalizing flows (`NFVI`).
 * Bill Engels added an extensive new Gaussian processes (`gp`) module. Standard GPs can be specified using either `Latent` or `Marginal` classes, depending on the nature of the underlying function. A Student-T process `TP` has been added. In order to accomodate larger datasets, approximate marginal Gaussian processes (`MarginalSparse`) have been added. 
 
 Documentation has been improved as the result of the project's monthly "docathons".
@@ -45,6 +45,8 @@ Parts of the test suite have been refactored.
 
 ### Fixes
 
+Fixed sampler stats error in NUTS for non-RAM backends
+
 Matplotlib is  no longer a hard dependency, making it easier to use in settings where installing Matplotlib is problematic. PyMC will only complain if plotting is attempted.
 
 Several bugs in the Gaussian process covariance were fixed.