This course provides the student with an introduction Bayesian analysis and compares Bayesian methods to that of frequentists. The course includes selection of prior distributions, computing posterior distributions, and conjugate models such as the Beta-Binomial, Normal-Normal, and Gamma-Poisson models. Bayesian inference using point and interval estimation, Bayesian hierarchical models, and exchangeability will be explored. Topics including Empirical Bayes versus a fully Bayes approach, Markov Chain Monte Carlo methods and model checking using Bayes factors and sensitivity analyses will be included. Prerequisite: A minimum grade of "B" in BIOS 9131, or permission from instructor.
Samawi, Hani, "BIOS 9231 - Bayesian Statistics I" (2016). Jiann-Ping Hsu College of Public Health Syllabi. 75.