Applied Bayesian Modeling and Prediction

Catalog description

Bayes rule, principles of Bayesian inference, Bayesian perspective on statistical models, posterior distribution computations using simulations, Markov Chain Monte Carlo (MCMC) (including Gibbs sampling, Metropolis-Hastings algorithm, slice sampler, hybrid forms and alternative algorithms), convergence monitoring and diagnosis, hierarchical models, model checking and model selection, and applications in the sciences using computer software such as R and WinBUGS.