Probabilistic Models

Catalog description

Prerequisite: A grade of “C” or better in MTH 286 and MTH 288, and a 300-level mathematics course, or departmental approval. This course is an introduction to probabilistic models. Course topics include: introduction to probability, Markov Chains in discrete and continuous time, including Poisson processes, and selected applications, such as Renewal Theory, Queueing Theory, Reliability, Brownian Motion, Simulation and Matrix Analytic Methods.