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/

# Tag Archives: pymc

## 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.

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:

http://stackoverflow.com/questions/24294203/difference-between-bugs-model-and-pymc/24347102#24347102

http://stackoverflow.com/questions/24242660/pymc3-multiple-observed-values/24271760#24271760

http://stackoverflow.com/questions/24402834/fitting-power-law-function-with-pymc/24413323#24413323

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## MCMC in Python: sampling in parallel with PyMC

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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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## MCMC in Python: How to set a custom prior with joint distribution on two parameters in PyMC

Question and answer on Stackoverflow. Motivated by question and answer on CrossValidated about modeling incidence rates.

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## MCMC in Python: random effects logistic regression in PyMC3

It has been a while since I visited my pymc-examples repository, but I got a request there a few weeks ago about the feasibility of upgrading the Seeds Example of a random effects logistic regression model for PyMC3. It turns out that this was not very time consuming, which must mean I’m starting to understand the changes between PyMC2 and PyMC3.

See them side-by-side here (PyMC2) and here (PyMC3).

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## Sequential Monte Carlo in PyMC?

I’ve been reading about Sequential Monte Carlo recently, and I think it will fit well into the PyMC3 framework. I will give it a try when I have a free minute, but maybe someone else will be inspired to try it first. This paper includes some pseudocode.

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## PyMC3 coming along

I have been watching the development of PyMC3 from a distance for some time now, and finally have had a chance to play around with it myself. It is coming along quite nicely! Here is a notebook Kyle posted to the mailing list recently which has a clean demonstration of using Normal and Laplace likelihoods in linear regression: http://nbviewer.ipython.org/c212194ecbd2ee050192/variable_selection.ipynb

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