Andrew Gelman: Introduction to Bayesian Data Analysis and Stan with Andrew Gelman

Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we will demonstrate the use of Stan for some small problems in sports ranking, nonlinear regression, mixture modeling, and decision analysis, to illustrate the general idea that Bayesian data analysis involves model building, model fitting, and model checking. One of our major motivations in building Stan is to efficiently fit complex models to data, and Stan has indeed been used for this purpose in social, biological, and physical sciences, engineering, and business. The purpose of the present webinar is to demonstrate using simple examples how one can directly specify and fit models in Stan and make logical decisions under uncertainty.

Andrew Gelman is a professor of statistics and political science at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari).

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