Lecture: 3 hours
Laboratory: 1 hour
Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis.
Prerequisite: STA 130B or STA 131B
Summary of course contents: