PHS 7055 - Bayesian Analysis

Bayesian inference is a statistical method that allows for the incorporation of prior beliefs about model parameters into an analysis, which can then be updated based on observed evidence. This course aims to offer an introductory overview of the fundamental elements of Bayesian data analysis, covering both conceptual and computational methods. The main topics presented include Bayes' theorem, prior and posterior distributions, and Markov chain Monte Carlo sampling methods. Our discussion will cover Bayesian methodologies, the selection of prior distributions, the summarization of posterior distributions, and model comparisons and adequacy.