Name: STAN Workshop
Start: 2017-07-22T10:00:00-04:00
End: 2017-07-22T17:00:00-04:00
Location: Viacom

Event space and lunch sponsored by Viacom

Stan (http://mc-stan.org (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.

Speaker Bios:
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.

Agenda:

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.

Requirements:
PyStan 2.16.0, http://pystan.readthedocs.io/en/latest/ matplotlibDownload Stan 2.16.0 Manual, 1.79 MB stan-reference-2.16.0.pdf (https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf)

Sinziana E.

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