Advance Bayesian Modelling with PyMC3


Dear all,

Last month I did a 2 days workshop in the Czech Republic (hosted by CEAi). They prepared professional video recording, which I would like to share here. This is a high-level PyMC3 workshop, as the attendees had already work through Introduction to Probabilistic programming (with PyMC3), which is built on top of the tutorials by @fonnesbeck. But the workshop also covers all the basics in depth. Due to the time limit, I did not manage to present more case studies - it would definitely be something to improve upon for next time :slight_smile:

The code and slide could be found below:

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

Fitting multiple measurements, shape issues in sample_ppc

This is awesome, Thank you Junpenglao!


This is very rich. Thank you for sharing.


This is awesome! Thank you for sharing!


Wow, this is phenomenal. Way to kill my weekend ^______^.


thank you sir!


Wow, amazing stuff, @junpenglao! Thank you for sharing with us :slight_smile:


This is so good, well done!


Lots of content. Thank you!