# Tag Archives: pymc

## MCMC in Python: a bake-off

While I’m on a microblogging spree, I’ve been meaning to link to this informative comparison of pymc, emcee, and pystan: http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/

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## MCMC in Python: Another thing that turns out to be hard

Here is an interesting StackOverflow question, about a model for data distributed as the sum of two uniforms with unknown support. I was surprised how hard it was for me.

http://stackoverflow.com/questions/24379868/estimate-the-parameters-of-a-random-variable-which-is-the-sum-of-uniform-random/24397044#24397044

I think the future of probabilistic programming should be to make a model for this easy to code.

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## MCMC in Python: Never… no… always check for convergence

I’ve had no teaching responsibilities over the last quarter, and I must miss it. I’ve found myself responding to PyMC questions on StackOverflow more than ever before. It is an interesting window into what is hard in Bayesian computation. Checking (and achieving) MCMC convergence is one thing that is hard. Here are some questions and answers that include it:

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## MCMC in Python: sampling in parallel with PyMC

Question and answer on Stack Overflow.

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## MCMC in Python: Estimating failure rates from observed data

A question and answer on CrossValidated, which make me reflect on the danger of knowing enough statistics to be dangerous.

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## MCMC in Python: How to make a custom sampler in PyMC

The PyMC documentation is a little slim on the topic of defining a custom sampler, and I had to figure it out for some DisMod work over the years. Here is a minimal example of how I did it, in answer to a CrossValidated question.

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