This three day course consists of six half-day modules and each module has three parts: Bayesian inference and computation; the Stan programming language; applied statistics.
Every participant will receive a copy of Andrew Gelman's landmark book Bayesian Data Analysis.
Proceeds support further development of Stan which is entirely open source.
Before class everyone should install R, RStudio and RStan on their computers. If problems occur please join the stan-users group and post any questions. It’s important that all participants get Stan running and bring their laptops to the course.
Class structure and example topics for the three days:
Monday, July 18: Introduction to Bayes and Stan
Intro to Bayes
Intro to Stan
The Statistical Crisis in Science
Stan by Example
Components of a Stan Program
Little data: how traditional statistical ideas remain relevant in a big data world
Tuesday, July 19: Computation, Monte Carlo and Applied Modeling
Computation with Monte Carlo Methods
Debugging in Stan
Generalizing from Sample to Population
Multilevel Regression and Generalized Linear Models
Computation and Inference in Stan
Why We Don't (Usually) Have to Worry about Multiple Comparisons
Wednesday, July 20: Advanced Stan and Big Data
Vectors, matrices, and transformations
Mixture models and complex data structures in Stan
Hierarchical Modeling and prior information
Bayesian Computation for Big Data
Advanced Stan programming
Open problems in Bayesian data analysis