Statistical Computing

Catalog description

Statistical Computing (3-0) Modern computational techniques and their application to various statistical models. Topics include Bayesian concepts such as prior and posterior distributions, Monte Carlo methods (such as MC integration and importance sampling), Markov chain Monte Carlo (MCMC) methods, including Gibbs sampling and Metropolis- Hastings algorithms. Additional topics may include Laplace approximation, methods for imputation of missing data, and bootstrapping. The techniques are applied to statistical methods such as linear regression, generalized linear models, capture-recapture models, mixture models and time series models.