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Stan: A Platform for Bayesian Inference

Stan is a high-level statistical modeling language designed for expressing a wide variety of models. By default, Stan performs full Bayesian inference using Markov chain Monte Carlo (MCMC). There are interfaces to many popular computing environments including R, Python, command line, Matlab, and Julia. Stan's default MCMC algorithm is an adaptive version of Hamiltonian Monte Carlo (HMC). HMC requires not only the computation of the (log) joint probability function, but also the gradients with respect to all parameters. This talk will describe the modeling language, provide some intuition for HMC and some of the implementation challenges, and a provide a couple examples using Stan. Stan is an open-source software project and relies on the contributions of many individuals, including Andrew Gelman, Bob Carpenter, Matt Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, Allen Riddell, Marco Inacio, Jeffrey Arnold, and Mitzi Morris.