A Short Course on Bayesian Modeling with Programming Language Stan in R
Bob Carpenter, Columbia University
Mitzi Morris, Columbia University
This short course will provide
* an introduction to Bayesian modeling
* an introduction to Monte Carlo methods for Bayesian inference
* an overview of the probabilistic programming language Stan
Stan provides a language for coding Bayesian models along with state-of-the-art inference algorithms based on gradients. There will be an overview of how Stan works, but the main focus will be on the RStan interface and building applied models.
The afternoon will be devoted to a case study of hierarchical modeling, the workhorse of applied Bayesian statistics. We will show how hierarchical models pool estimates toward the population means based on population variance and how this automatically estimates regularization and adjusts for multiple comparisons. The focus will be on probabilistic inference, and in particular on testing posterior predictive calibration and the sharpness of predictions.
You should show up with RStan installed. Instructions are linked from here:
Warning: follow the instructions step-by-step; even though installiation involves a CRAN package, it's more complex than just installing from RStudio because a C++ toolchain is required at runtime.
If you run into trouble, please ask for help on our forums---they're very friendly:
Thursday, October 19, 2017
10:00-11:00am Open Seminar - Introduction to the "Stan" System
11:30am-1:00pm Tutorial part 1
1:00pm -2:00pm Lunch Break
2:00pm -3:30pm Tutorial part 2
3:30pm -3:45pm Break
3:45pm -5:30pm Tutorial part 3
Instructors' Short Bio:
Bob Carpenter is a research scientist in computational statistics (Columbia University), where he developed the Stan language and remains one of its core developers. Bob has a Ph.D. in computer science. He has been a professor and worked in industry in the area of programming languages, linguistics, speech recognition, and natural language processing.
Mitzi Morris is a staff programmer in computational statistics (Columbia University), where she is one of the core Stan language and math library developers. Mitzi has an M.S. in linguistics. She previously worked in industry and academia on search, natural language processing, and bioinformatics.
Host: Vlado Keselj (email@example.com)
Room 430, Goldberg Computer Science Building
$15 student fee or $30 regular feeA Short Course on Bayesian Modeling with Programming Language Stan in R