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Advance Bayesian Modelling with PyMC3

This repository contains notebooks, slides, and videos for my workshop in May 2018 @CEAi in the Czech Republic.

Background preparation

The attendees had work through Introduction to Probabilistic programming (with PyMC3), which is built on top of the tutorials by Chris Fonnesbeck. Basic knowledge of scientific computation in Python and PyMC3 is required.

Video content

Session 1: Probabilistic thinking: generative model and likelihood computation
Session 2: Likelihood in PyMC3 and model reparameterization
Session 3: Model parameterization and coordinate system: Neal's funnel
Session 4: Bayesian modelling and inference with MCMC in PyMC3
Session 5: Model evaluation and model comparison
Session 6: Case study: modelling multivariate observation
Session 7: Mixing MCMC samplers: Compound step in PyMC3