In X-ray crystallography, the most important prior distributions include two special cases of the generalized gamma distribtion. I am very keen to try this parameterization of the variational distritribution in my research project. How hard would it be for the TFP devs to implement this distr

Summary:

It'd be nice to have a builder pattern for var contexts to make them easy to construct for testing. Something that could be used like this:

MatrixXd m(3, 2); 
...
var_context vc
  = var_context::builder()
    .matrix("a", m)
    .real("f", 2.3)
    .build();

Current Version:

v2.23.0

The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.

E.g.

• gradient descent with momentum
• adaptive learning rate gradient descent, e.g. AdaGrad

(Note both the links are for stochastic gradient descent, but we'd be implementing a simpler, non-stochastic one)

Won't be brilliant, but can be very informa

This blogpost from Lyndon White mentions several antipatterns for Julia code: https://white.ucc.asn.au/2020/04/19/Julia-Antipatterns.html (thanks @bauglir for pointing this out). Some of the antipatterns mentioned here are also present in the FL code.

1. The most prominent one is the over-constraining of argument types. Some very specific constraints are needed for the update rules, but in oth