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Upcoming talk: Bayesian estimation with Markov Chain Monte Carlo using PyMC

From: Drew C.
Sent on: Saturday, September 18, 2010 1:26 PM

Good afternoon! ?As some of you know, I will be presenting an introduction to NetworkX at the end of the month, but I am also the organizer for the NYC R?statistical?programming meetup (

Given the recent interest in Python and machine learning some members have expressed I wanted to invite all of your to our November meetup, which will cover Bayesian estimation with Markov Chain Monte Carlo in Python using PyMC?( ?Below is a brief description of the presentation.

Thanks to the New York Marathon, we are very lucky to have Chris Fonnesbeck, instructor in the Department of Biostatistics at Vanderbilt University, in town in early November. Chris has been kind enough to offer to present his work on Bayesian estimation using MCMC, despite the fact that he will no doubt be exhausted from having run a marathon the day before.

Chris is one of the co-creators of PyMC, the premier package for MCMC estimation in Python. From PyMC's website:

Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. PyMC is a python module that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

You can RSVP for the meetup here:

While Chris's presentation will focus primarily?on Python, I may also present a short follow-up talk on how to bridge R from Python to add to the analysis. ?I hope some of you will join us, as I think it will be a great opportunity four our groups to mingle.

- Drew
Drew Conway
Ph.D. Student
Department of Politics, New York University
[address removed]

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