Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 130B or STA 131B. 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. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.