Applied Stochastic Processes with Simulation

Catalog description

Covers joint distributions, covariance, correlation, sums of random variables, strong law of large numbers, central limit theorem, Monte Carlo simulation, Poisson processes, Markov chains, Markov chain Monte Carlo algorithms, including Gibbs sampling and Metropolis-Hastings algorithms. Includes optional topics: Brownian motion and applications to Bayesian inference. Enrollment Requirement(s): for graduate students: CS 111 and ( MATH 264 or MATH 364 ) and ( MATH 342 or MATH 441 ), all with grades of C (2.0) or better. Prerequisite(s): for undergraduates : CS 111 and ( MATH 264 or MATH 364 ) and ( MATH 342 or MATH 441 ), all with grades of C (2.0) or better.