STATISTICS 225 - Bayesian Statistical Analysis

Last offering was Fall 2018

Next scheduled offering in 2019-2020

   instructor: Hal Stern
       office: 2216 Bren Hall
        phone: 949-824-1568
        email: sternh@uci.edu





Lecture notes:

These files are sets of slides that serve
as "approximate" lecture notes.

Slides #1 - Introduction, univariate models, multivariate models, large sample results
Slides #2 - Hierarchical models, Bayesian computing
Slides #3 - Computation
Slides #4 - Model checking, model comparison, model selection, classical vs Bayes
Slides #5 - Robust models, regression models (regular and hierarchical), data collection


Sample code 

One parameter example in R
Logistic regression (2-parameter example) in R (grid sampling, Stan)
Stan code for the logistic regression example

Normal-normal hierarchical model (8 schools) in R (grid sampling, Stan)
Stan code for the normal-normal hierarchical model
Beta-binomial hierarchical model (70 rat studies) in R (grid sampling, Stan)
Stan code for beta-binomial w/ default prior
Stan code for beta-binomial w/ informative prior

R code to compute Gelman & Rubin convergence diagnostics
Gibbs sampling for normal-normal model in R
Finding marginal posterior mode for normal-normal model in R (used to identify starting points for Metropolis algorithm)
Metropolis algorithm for normal-normal model in R







Return to Hal Stern's homepage
Return to UCI Statistics page