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Monday, June 27 • 2:30pm - 4:00pm
An Introduction to Bayesian Inference using R Interfaces to Stan (Part 2)

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The Stan project implements a probabalistic programming language, a library of mathematical and statistical functions, and a variety of algorithms to estimate statistical models in order to make Bayesian inferences from data. The three main sections of this tutorial will

  1. Provide an introduction to modern Bayesian inference using Hamiltonian Markov Chain Monte Carlo (MCMC) as implemented in Stan.
  2. Teach the process of Bayesian inference using the rstanarm R package, which comes with all the necessary functions to support a handful of applied regression models that can be called by passing a formula and data.frame as the first two arguments (just like for glm).
  3. Demonstrate the power of the Stan language, which allows users to write a text file defining their own posterior distributions. The stan function in the rstan R package parses this file into C++, which is then compiled and executed in order to sample from the posterior distribution via MCMC.
For details, refer to tutorial description.

Speakers
avatar for Ben Goodrich

Ben Goodrich

Lecturer in the Discipline of Political Science, Columbia University
Ben Goodrich is a core developer of Stan, which is a collection of statistical software for Bayesian estimation of models, and is the maintainer of the corresponding rstan and rstanarm R packages. He teaches in the political science department and in the Quantitative Methods in the Social Sciences master's program at Columbia University.


Monday June 27, 2016 2:30pm - 4:00pm
SIEPR 130 366 Galvez St, Stanford, CA 94305

Attendees (82)