Event space and lunch sponsored by Viacom
Stan (http://mc-stan.org) is a statistical modeling platform used by thousands of scientists, engineers, and other researchers for statistical modeling, data analysis, and prediction. It is being applied academically and commercially across fields as diverse as ecology, pharmacometrics, physics, political science, finance and econometrics, professional sports, real estate, publishing, recommender systems, and educational testing.
In this workshop we’ll review the foundations of Bayesian inference and computation, playing specific emphasis on the details critical to robust statistical analyses. We’ll then demonstrate the implementation of these methods in Stan with interactive examples, beginning with parametric regression and classification before considering their Gaussian process equivalents.
Michael Betancourt is a research scientist in the Applied Statistics Center at Columbia University, where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan he also collaborates on analyses in, amongst others, epidemiology, pharmacology, and physics.
Mitzi Morris is a member of the Stan development team. Her background is in natural language processing and bioinformatics. Her editor is emacs.
10:00 - 11:00 Foundations of Bayesian Inference and Computation
11:00 - 11:30 Introduction to Stan
11:30 - 12:00 Linear Regression
12:00 - 01:00 Lunch
01:00 - 01:30 Linear Regression (cont)
01:30 - 02:30 Logistic Regression
02:30 - 03:00 Introduction to Gaussian Processes
03:00 - 04:00 Gaussian Process Regression
04:00 - 05:00 Gaussian Process Classification
Most of the course will be interactive examples so the schedule will adapt to the students’ speed.