(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Speaker: Andrew Gelman, Columbia University
Date: Thursday, October 11 2012
Time: 4:00PM to 5:00PM
Host: Polina Golland, CSAIL
Contact: Polina Golland, 6172538005, firstname.lastname@example.org
Stan (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis.
P.S. Here’s the talk.
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