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BZAN 625 - Bayesian Modeling and Computations

3 credit hours

Bayes theorem, prior and posterior distributions, inference methods such as posterior means and HPD regions, Monte Carlo inference including Gibbs sampling, the Metropolis-Hastings algorithm, and importance sampling. Bayesian analysis of linear and non-linear regression models, model selection using Bayes factors. Predictive inference based on posterior distributions, and selected applications drawn from business and scientific settings.

  • 615 or Statistics 615.
  • Minimum student level – graduate.