8 August 2019
Learn how to implement Bayesian workflows using CmdStanPy (a Python interface for Stan). In this hands-on workshop, we will be working with a very fun (surprise!) dataset and make predictions using Bayesian methods.
CmdStanPy allows pythonistas to add the power of Bayesian inference to their toolkit via a small set of functions and objects designed to use minimal memory and parallelize computation. Given a dataset and a statistical model written as a Stan program, CmdStanPy compiles the model, runs Stan’s MCMC sampler (via CmdStan) to obtain a sample from the posterior, and assembles this sample as a numpy nd-array or pandas.dataframe for downstream visualization and analysis.