

How to be a Bayesian in Python - mathattack
http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/

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tel
Stan is definitely the project you'll want to follow if you're going to be
doing this sort of thing a lot. It's built by Andrew Gelman out of Columbia
who is an expert in hierarchical Bayesian methods and has done lots of big
models for social sciences (and I believe was responsible for the original NYT
538 model).

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.

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BoppreH
For lower-level use of Bayes I have a library that can be helpful:
[https://github.com/boppreh/bayesian](https://github.com/boppreh/bayesian) ,
or "pip install bayesian". It has simple belief updates and even some
classification methods.

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new_test
Is that prior ever used in practice? How does it generalize to several
predictors and interaction terms?

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glamp
another great post from jakevdp. thanks jake!

