A Beginner's Guide to Bayesian Modeling with PyMC

What you'll build

• Your first Bayesian model in PyMC, from prior to posterior, on a real dataset
• A reusable notebook workflow for defining, fitting, and checking a model
• Posterior plots and summaries you can actually explain to a stakeholder

What you'll learn

• What a prior, likelihood, and posterior are, in plain terms and in code
• How MCMC sampling works well enough to trust and debug your results
• How to check a model with posterior predictive checks instead of guessing
• When Bayesian modeling earns its keep over a classical approach

About Chris

Adjoint Associate Professor of Biostatistics at Vanderbilt University Medical Center, with 20 years across academia and industry and pro-baseball R&D for the Phillies, Yankees, and Brewers.