bayesian-inference

NumPyro now has several excellent introductory examples with no direct counterparts in Pyro. Porting one of these to Pyro would be a great way for someone to simultaneously learn more about Bayesian data analysis and make a valuable open source contribution.

If you are reading this and want to give one of them a try, please leave a comment here so that other peo

Description:

We need some easy to follow instructions on how to use the core Stan inside a user-written C++ program. See stan-dev/stan#3085 for example.

The instruction can simply guide through the task of compiling one of the models and running MCMC with the services. The biggest challenges are typically all the dependencies that we need to include in the C++

trace_to_dataframe() in PyMC3 to save traces is currently implemented in Rethinking_2 notebooks (e.g. Chp_04). But the function is planned for deprecation, with Arviz being the intended package to save traces. As per this comment by @AlexAndorra, Arviz's InferenceData format is a superior replacement to this function as it

A quick search for mixture distributions in numpyro only turns up examples using Categorical in conjunction with an array of distributions. Since sampling from discrete distributions is not always desirable, I have implemented a quick general purpose mixture distribution with continuous log probability.

class Mixture(Distribution):
    arg_constraints = {}

    def __init__(self

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

Hi,

is there any plan to implement the Generalized Pareto Distribution in brms (paul-buerkner/brms#110 (comment))? I am playing around with an extreme values analysis and it looks like extremes collected as Peak Over Threshold are better represented by the GPD instead of the generalized extreme value distribution, which I am so happy to see already in `b