STAT 768 - Applied Bayesian Modeling and Prediction

Credits: 3

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.

Requisites
Prerequisites: (STAT 705 or STAT 713), and (STAT 510 or STAT 770).

Typically Offered
Spring-Odd Years

KSIS Course Search

Kansas State University

Course Catalog

STAT 768 - Applied Bayesian Modeling and Prediction