How to be a Bayesian in Python(jakevdp.github.io)
jakevdp.github.io
How to be a Bayesian in Python
http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
4 comments
For lower-level use of Bayes I have a library that can be helpful: https://github.com/boppreh/bayesian , or "pip install bayesian". It has simple belief updates and even some classification methods.
Is that prior ever used in practice? How does it generalize to several predictors and interaction terms?
another great post from jakevdp. thanks jake!
Stan uses the really clever No-U-Turn sampler algorithm which will help a lot in highly correlated models (where sampling tends to take much, much longer to converge).
Edit: Also, if anyone is interested in learning more about MCMC samplers then reading up on NUTS is a good idea. The basic material there does a great job both getting ideas about Hamiltonian MCMC out in the air and also talking about how to do some tricky optimizations to the algorithm while retaining its probabilistic properties.