— Bayesian Statistical Inference

Prerequisites & course information

This version has ended; see updated course, below. Course Description: 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. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 145 — Bayesian Statistical Inference (4 units) Course Description: 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(s): STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Winter Quarter 2027. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 145 — Bayesian Statistical Inference (4 units) Course Description: 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(s): STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Winter Quarter 2027.