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


This is awesome, Thank you Junpenglao!

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This is very rich. Thank you for sharing.

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This is awesome! Thank you for sharing!

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

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thank you sir!

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

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This is so good, well done!

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Lots of content. Thank you!

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Wow, thank you @junpenglao! Exactly the type of resources I was looking for :slight_smile:
Can’t wait to go for it, and hopefully I’ll be able to give you a constructive feedback after it :wink:


A small feedback, having read the notebooks and watched the videos: it’s a really useful ressource and I highly recommend it to anyone who’s already used PyMC3 and is looking for a way to undestand what’s going on under the hood (I would not recommend it for beginners though. Take a look at Chris Fonnesbeck’s tutorial at PyCon 2017 first).
@junpenglao really goes into the weeds of the library, explaining in details how you generate models, compute likelihoods, do inference and evaluate/debug your models. It gives you an understanding of how PyMC3 works and more generally teaches you core concepts of Bayesian modeling. Well done and thank you for sharing!!

On a more practical note, I would recommend reading the notebooks before watching the videos, to get an intuition of the topics and make the most out of the lessons.
I would say sessions 2, 4 and 5 are must-sees, with session 3 perhaps being the less practically useful (at least to me) because of its complexity - interesting for general knowledge though.
Hope this feedback will help some, and thanks again for this amazing content JunpengLao!


Wow thanks for the kind words Alex! I am really glad that you find it helpful!

Also, I wrote a developer guide of pymc3 largely based on my experience of preparing for this workshop, you might also find it helpful


Great, thanks, gonna take a look at that :wink:

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Hi @junpenglao, the videos and materials are extremely helpful. Thanks a lot for sharing it :+1: :+1: :+1:

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