(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
This post is by Eric.
We are starting a series of free webinars about Stan, Bayesian inference, decision theory, and model building. The first webinar will be held on Tuesday, October 25 at 11:00 AM EDT. You can register here.
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.
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Update: a video recording of the webinar is now available here.
The post Webinar: Introduction to Bayesian Data Analysis and Stan appeared first on Statistical Modeling, Causal Inference, and Social Science.
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