Tag Archives: MCMC

MCMC in Python: sim and fit with same model

Here is a github issue and solution that I saw the other day. I think it’s a nice pattern.

def generate_model(values={'mu': true_param, 'm': None}):

    mu = pymc.Uniform("mu", lower=-10, upper=10, value=values['mu'], 
        observed=(values['mu'] is not None))

    # likelihood function
    m = pymc.Normal("m", mu=mu, tau=tau, value=values['m'], 
        observed=(values['m'] is not None))

    return locals()


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MCMC in Python: Fit a non-linear function with PyMC

Here is a recent q&a on stack overflow that I did and liked.

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The one before that

Jake Vanderplas’s comparison of Python MCMC modules was preceded by a Bayesian polemic. In general, I find the stats philosophy war old-timey and distracting, but his comparison of confidence intervals and credible intervals is something I need to understand better.


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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.


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: 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 Convergence Diagnostics

I have revisited my approach to deciding if MCMC has run for long enough recently, and I’m collecting some of the relevant material here:

Last time I thought about it: http://healthyalgorithms.com/2010/04/19/practical-mcmc-advice-when-to-stop/

Original paper for R_hat approach: http://www.stat.columbia.edu/~gelman/research/published/itsim.pdf

Presentation comparing several approaches: http://www.people.fas.harvard.edu/~plam/teaching/methods/convergence/convergence_print.pdf

Published comparison: http://www.jstor.org/stable/2291683

Blog about a cool visual approach: http://andrewgelman.com/2009/12/24/visualizations_1/

Discussion on cross-validated: http://stats.stackexchange.com/questions/507/what-is-the-best-method-for-checking-convergence-in-mcmc

Book with a chapter on this referenced there: http://www.amazon.com/dp/1441915753/?tag=stackoverfl08-20 (available as an eBook from UW Library, how convenient!)

Another related blog: http://xianblog.wordpress.com/2012/11/28/mcmc-convergence-assessment/

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ML in Python: Naive Bayes the hard way

A recent question on the PyMC mailing list inspired me to make a really inefficient version of the Naive Bayes classifier. Enjoy.

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