IE 515  Stochastic Process Modeling  An introduction to the use of stochastic processes in the modeling of physical and natural systems. Use of generating functions, conditional probability and expectation, Poisson processes, random walk models, Markov chains, branching processes, Markov processes, and queuing processes in an applied setting. Requires an elementary knowledge of calculusbased probability and statistics (e.g., IE 311) as well as knowledge of differential equations (e.g. MATH 392). (3 cr.)
A stochastic process is a mathematical model that can represent the behavior of complicated systems in very realistic ways. In practice, these models are constructed by putting together simple elements in a systematic way, allowing one to infer the behavior of the whole from that of its parts. Although there are applications of this technique in virtually every form of applied science, from agriculture, to medicine, to all sorts of engineering, this offering focuses primarily on those applications of special interest to engineers. These topics include:
To effeicently and effectively discuss these topics, I assume that you have had a calculus based course in probability and statistics (IE 311, or equivalent) and engineering mathematics through differential equations (MATH 392). While the approach is advanced and you will have to deal with interesting mathematics, the emphasis is on knowing (rather than mathematically proving) results. To this end, we will use MatLab heavily to:
No preknowledge of either software is presumed, though experience in MatLab, or a similar package, would be helpful. MatLab is available in the IE computer lab.
Instructor:  John Mullen, Tel:(505)6462958, email: jomullen@nmsu.edu 
Text:  Elements of Applied Stochastic Processes, 3rd Ed., by U. N. Bhat and G. K. Miller. 2002. ISBN: 0471414425 
Computer: 

Revision Date: December 15, 2005 by jpm.