Pystan: Bayesian Inference for Fun and Profit

Stephen Hoover
http://mdp.cdm.depaul.edu/DePy2016/default/schedule
Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.

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