Hands-on example: Bayesian linear regression; prior predictive checks; posterior sampling with NUTS; basic model diagnostics; posterior predictive checks.

Addressing frequently asked questions; debugging convergence issues; understanding and fixing divergences; performance optimization tips.

How AI/LLMs are changing PyMC workflows; PyMC's development roadmap; opportunities for contribution.

Recommended installation procedure; PyMC's computational backends; troubleshooting common installation issues; setting up development environments.

This one-hour tutorial introduces new users to version 5 of PyMC, a powerful Python, open source library for probabilistic programming and Bayesian statistical modeling. Participants will learn the fundamentals of PyMC, best practices for installation and setup, and gain hands-on experience building their first Bayesian model.

Overview of PyMC and its role in the Python data science ecosystem; understanding probabilistic vs Bayesian approaches; a survey of the probabilistic programming landscape; real-world applications and case studies.

Model contexts and random variables; prior and likelihood specification; working with observed data; PyMC's relationship with ArviZ.

ArviZ for visualization and diagnostics; related packages (Bambi, PyMC-experimental); finding and using PyMC example notebooks; community resources and support channels.

Outline: The Bayesian framework; Probabilistic programming; Creating scientific software packages; PyMC.