Constant Stochastic Gradient Descent (pymc-devs#2544)

* add csg

* Fig 1 and likelihood plotted

* posterior comparison

* csg nb and python file updated

* ConstantStochasticGradient renamed as CSG

* inserted update in RELEASE-NOTES

* nb updated and added to examples

RELEASE-NOTES.md

@@ -7,7 +7,8 @@
 
 - Improve NUTS initialization `advi+adapt_diag_grad` and add `jitter+adapt_diag_grad` (#2643) 
 - Update loo, new improved algorithm (#2730)
-
+- New CSG (Constant Stochastic Gradient) approximate posterior sampling
+  algorithm (#2544)
 ### Fixes 
 - Fixed `compareplot` to use `loo` output. 
 - Add test for `model.logp_array` and `model.bijection` (#2724) 
@@ -237,6 +238,7 @@ Taku Yoshioka <taku.yoshioka.4096@gmail.com>
 Peadar Coyle (springcoil) <peadarcoyle@googlemail.com>
 Austin Rochford <arochford@monetate.com>
 Osvaldo Martin <aloctavodia@gmail.com>
+Shashank Shekhar <shashank.f1@gmail.com>
 
 In addition, the following community members contributed to this release:
 

docs/source/examples.rst

@@ -78,3 +78,12 @@ Variational Inference
    notebooks/convolutional_vae_keras_advi.ipynb
    notebooks/empirical-approx-overview.ipynb
    notebooks/normalizing_flows_overview.ipynb
+
+
+Stochastic Gradient
+===================
+
+.. toctree::
+   notebooks/constant_stochastic_gradient.ipynb
+   notebooks/sgfs_simple_optimization.ipynb
+   notebooks/bayesian_neural_network_with_sgfs.ipynb