Category Archives: statistics

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}):

    #prior
    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.

http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/

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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.

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:

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: 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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Journal Club: Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Last week, we read Chien et al, Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.  I’ve been very interested in these sort of “small-areas” spatial statistical methods recently, so it was good to see what is out there as the state of the art.  I think I’ve got something to contribute along these lines some day soon.

Maps in small multiples look just lovely, too:
usa

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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).

pymc3

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IHME Seminar: Unifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs)

Thomas Richardson gave a recent seminar at IHME about how the potential outcomes crowd can make sense of graphical models and vice versa. It also has a CSSS working paper to complement it, a trend in our recent seminars: http://www.csss.washington.edu/Papers/wp128.pdf

This talk had some great graphics, as I would hope for in a talk on graphical modeling:
graphical_model

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