Bayesian Data Analysis, ICPSR 2014
Stat* Theatre, Ann Arbor

Doing Bayesian Data Analysis

A four-day course offered through the
Interuniversity Consortium for Political
and Social Research (ICPSR) Summer Program

July 8 - July 11, 2014
University of Michigan, Ann Arbor

Stat* Theatre, Ann Arbor

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides complete information about the relative credibilities of all candidate parameter values for any descriptive model of the data. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. This course shows you how to do Bayesian data analysis, hands on (with free software called R and JAGS). The course will use new programs and examples.

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis. No specific mathematical expertise is presumed. In particular, no matrix algebra is used in the course. Some previous familiarity with statistical methods such as a t-test or linear regression can be helpful, as is some previous experience with programming in any computer language, but these are not critical.

This course is offered through the Interuniversity Consortium for Political and Social Research (ICPSR) Summer Program. Registration is required and links are provided below.

Course Topics include the following. There will be updated software and examples for 2014!
A posterior probability distribution for parameters that describe two groups, showing complete distributions of the difference of means (right middle), the difference of standard deviations, the effect size (right bottom), and posterior predictive check (right upper).