Materials for thoroughly understanding Stan

[miles]

Hi @kokorap27!

@kokorap27

Over the last years I’ve been using Stan for implementing Bayesian inference, however, I haven’t learnt Stan’s syntax and how it really works behind the scenes, and thus, I get stuck with some Stan ‘subtlety’ (it actually means my lack of understanding) a lot of times. Could someone please point me to the correct material for starting on properly learning Stan at once?

Is it Stan per se (i.e. how Stan constructs sums of log densities rather than more graphical paradigms as in PyMC3) or is it just the probabilistic programming language itself? For the later, I do really think the user guide and reference manual are great. If you’ve already spent a good deal of time over there, I’d also like to recommend the Stan case studies and tutorials where you may find some implementations (including discussion of optimizations and pitfalls) that are relatable to you and your research. In general, Richard McElreath’s Statistical Rethinking book and video lectures are hard not to recommend for a soft introduction to Stan & working with NUTS-HMC.

@kokorap27

I’d also appreciate suggestions on material for understanding how Stan actually works as to at least get an idea of the primary reasons for divergence.

I’m assuming you’d like recommendations to understand the HMC broadly, to understand how pathologies may arise and how they relate to divergent transitions. For that, I highly recommend starting here: Michael Betancourt. 2017. “A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv 1701.02434. [1701.02434] A Conceptual Introduction to Hamiltonian Monte Carlo.

@martinmodrak also has a great post on the forums here to supplement Divergent transitions - a primer