Come learn Bayesian Data Analysis (BDA) and Markov chain Monte Carlo (MCMC) computation using Stan with Bayes Master Andrew Gelman and Stan developers Bob Carpenter and Daniel Lee.

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:

Sunday, July 19: Introduction to Bayes and Stan

Morning:

Intro to Bayes

Intro to Stan

The Statistical Crisis in Science

Afternoon:

Stan by Example

Components of a Stan Program

Little data: how traditional statistical ideas remain relevant in a big data world

Monday, July 20: Computation, Monte Carlo and Applied Modeling

Morning:

Computation with Monte Carlo Methods

Debugging in Stan

Generalizing from Sample to Population

Afternoon:

Multilevel Regression and Generalized Linear Models

Computation and Inference in Stan

Why We Don't (Usually) Have to Worry about Multiple Comparisons

Tuesday, July 21: Advanced Stan and Big Data

Morning:

Vectors, matrices, and transformations

Mixture models and complex data structures in Stan

Hierarchical Modeling and prior information

Afternoon:

Bayesian Computation for Big Data

Advanced Stan programming

Open problems in Bayesian data analysis