## instructor: Hal Stern office: 2216 Bren Hall phone: 949-824-1568 email: sternh@uci.edu office hours: M 3:00-4:00pm

Course documents will be made available here:Syllabus Computing handoutExamples from class (including R 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 Variational Bayes output displays (from text) Hierarchical regression exampleHomework assignments:Homework 1 (assigned 10/4, due 10/16) Solutions and R code/ouput (posted 10/25) Homework 2 (assigned 10/18, due 10/30) Solutions and R code/output (posted 11/4) Homework 3 (assigned 11/1, due 11/15) Note: Problem 1 of HW 3 is due 11/8 (it concerns project proposals) Solutions and code (Metropolis, Gibbs) Homework 4 (assigned 11/15, due 11/29) Solutions and codeExam information:Old exam Solutions to old exam Exam Solutions to examLecture notes:These files are sets of slides that serve as "approximate" lecture notes. Each set should last about two-three weeks. Slides #1 - Introduction, univariate models, multivariate models, large sample results Slides #2 - Hierarchical models, Bayesian computing, model checking, classical vs Bayes Slides #3 - Robust models, regression models (regular and hierarchical), data collection

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