Probability and Statistical Models
Theory, modeling, and computational topics in probability and statistics: distribution theory and modeling, simulation and applied probability models in statistics, generation of random variables. Monte Carlo method and integration; Markov Chain Monte Carlo methods; applied stochastic processes including Markov process theory, linear systems theory, and AR models. Latent variable probability models, i.e., mixture models, hidden Markov models, and missing data problems. Discrete and continuous multivariate distributions; linear, multinormal, and graphical models; tools of linear algebra and probability calculus. Statistical computing using Matlab/R. Prerequisite: Statistical Science 601 and 721. Recommended prerequisite: Statistical Science 732.