STOCHASTIC PROCESSES

Graduate

This course is an introduction to the branch of probability theory that deals with the analysis of systems that evolve over time. Topics include random walks, Markov chains, Poisson processes, continuous time Markov chains, birth and death processes, exponential models, and applications of Markov chains. Graduate students will be expected to acquire additional knowledge on discrete time martingales and their applications. In all course assessments, the graduate students will be held to a higher standard, including a graduate level project on Hidden Markov Chains, Brownian Motion, convergence of probability measures or other advanced topics. This course is cross-level listed with STAT 4410. Credit cannot be received for both MCS 8410 and STAT 4410. PREREQUISITE: Admission to a graduate program in Science HOURS OF CREDIT: 3