MCMC in Python: PyMC for Bayesian Model Selection

(Updated 9/2/2009)
I never took a statistics class, so I only know the kind of statistics you learn on the street. But now that I’m in global health research, I’ve been doing a lot of on-the-job learning. This post is about something I’ve been reading about recently, how to decide if a simple statistical [...]

Numbers from World TB Day + Maps of Malaria

Twice as many people were diagnosed with both HIV and tuberculosis in 2007 than were in 2006. (Science Mag, BMJ)
Quote about global health data in the article that is quite consistent with what I’ve seen comes from Richard Chaisson:
“They’re working with the best stuff they have, and the best stuff they have is not [...]

MCMC in Python: PyMC for Bayesian Probability

I’ve got an urge to write another introductory tutorial for the Python MCMC package PyMC.  This time, I say enough to the comfortable realm of Markov Chains for their own sake.  In this tutorial, I’ll test the waters of Bayesian probability.
Now, what better problem to stick my toe in than the one that inspired Reverend [...]

MCMC in Python: PyMC to sample uniformly from a convex body

This post is a little tutorial on how to use PyMC to sample points uniformly at random from a convex body.  This computational challenge says: if you have a magic box which will tell you yes/no when you ask, “Is this point (in n-dimensions) in the convex set S”, can you come up with a [...]

MCMC: Running a chain, making it look easy

As I was saying in my last post, I’ve been getting interested in actually running Markov Chain Monte Carlo algorithms, instead of trying to prove things about their asymptotic performance. It seems like the “stats” way to do this is to use R and WinBUGS, but I’ve always thought that R programming looks messy. [...]