Introduction to Bayesian Methods
Instructor: Gary RosnerDescription:
Illustrates current approaches to Bayesian modeling and computation in statistics. Describes simple familiar models, such as those based on normal and binomial distributions, to illustrate concepts such as conjugate and noninformative prior distributions. Covers advanced tools, including linear regression, hierarchical models (random effect models), generalized linear models, and mixed models. Discusses aspects of modern Bayesian computational methods, including Markov Chain Monte Carlo methods (Gibbs' sampler and Metropolis Hastings algorithm) and their implementation and monitoring, and examples of real statistical analyses.