Bayesian Data Analysis

Catalog description

Fundamentals of Bayesian data analysis, methodologies, and applications. Topics include Bayes Theorem and the Bayesian approach, differences between Bayesian and Frequentist inference: estimating confidence intervals and high-density intervals and their interpretation, difference between posterior and prior distributions, selection of appropriate priors, Markov Chain Monte Carlo (MCMC) and Gibbs Sampling techniques, Hypothesis testing (and interval estimation) in Bayesian approaches, and model selection criteria and model fitting using data. Software such as R/Python and JAG/Stan will be used.