Bayesian Inference

Catalog description

This course examines theoretical foundations and applications of Bayesian paradigm, including Bayes' theorem, prior distribution, likelihood function, deriving posterior distributions, and point and interval estimations. A variety of topics are covered, which encompass Bayesian inference for single- and multi-parameter models, linear regression, hierarchical models, and commonly used Gibbs sampler and Metropolis-Hastings algorithm. Assessment of convergence, the evaluation of models, and the presentation of the results are also illustrated. Real world examples drawn from medical research are used to show practicality of Bayesian approach, particularly how to update beliefs and make inferences from observed data. Elementary knowledge of the use of statistical computing packages is needed.