Course Description
Stochastic processes including discrete, continuous and conditional probability concepts. Definitions and properties of stochastic processes. Markov processes and chains, basic properties, transition matrices and steady state properties. Reliability renewal and queueing processes, expected waiting times, single and multiserver queues.
Additional Requirements for Graduate Students:
Additional and/or alternative problems of a more challenging
nature will be required for graduate students on homework and
exams.
Athena Title
Applied Stochastic Processes
Prerequisite
(MATH 2270 or MATH 2500) and (MATH 3300 or MATH 3000) and (STAT 4510/6510 or MATH 4600/6600)
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
This course is an introduction to stochastic processes that focuses on some of the classes of processes that are most commonly encountered in applications. Goals of the course include understanding the basic theory underlying these processes and applying them to model real-world phenomena.
Topical Outline
Probability review, basic ideas of stochastic processes, Poisson processes, Markov chains, stationary distributions of Markov chains, Markov processes, queuing processes, Brownian motion, renewal theory.