Applied Bayesian Statistics.

Catalog description

Semester course; 3 lecture hours. 3 credits. Enrollment requires permission of instructor. Students should be familiar with statistical techniques such as multiple linear regression and multi-way ANOVA. Basic probability theory, prior distributions, prior distribution elicitation, likelihood principle, conjugate prior distributions, posterior probability distributions, Bayesian inference. Analysis of typical types of experiments such as single sample experiments, two sample experiments, regression analysis, ANOVA, hierarchical models, structural equation modeling and other topics. Software such as R, Python, JAGS or STAN will be used to perform computations.