PyMC2 has some tricky tricks for reducing function evaluations if possible. A question asked and answered on Stack Overflow investigates: http://stackoverflow.com/q/27714635/1935494 and I made a IPython Notebook with more details, too: http://nbviewer.ipython.org/gist/aflaxman/c07c5261bf22f6847098

# Tag Archives: pymc

## A little PyMC2 trick

Here is a little trick for getting around a pesky initialization issue in PyMC2 models, asked and answers on Stack Overflow when thing were quiet around here: http://stackoverflow.com/a/27724637/1935494

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## PyMC3 with PyMC2

Did you know I have a fork of PyMC3 that you can run at the same time as PyMC2? I don’t keep it up to date, but people seem to want it every once in a while. Maybe this will help someone find it: https://github.com/aflaxman/pymc

import pymc as pm2 import pymc3 as pm3

Good for head-to-head comparisons…

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## Bayesian Correlation in PyMC

Here is a StackOverflow question with a nice figure:

Is there a nice, simple reference for just what exactly these graphical model figures mean? I want more of them.

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## MCMC in Python: observed data for a sum of random variables in PyMC

I like answering PyMC questions on Stack Overflow, but sometimes I give an answer and end up the one with the question. Like what would you model as the sum of a Poisson and a Negative Binomial?

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## 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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## 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:

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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