OR / STAT 645
Stochastic Processes

John Shortle
Systems Engineering and Operations Research
George Mason University

Many real-world processes are fundamentally stochastic that is, they have some degree of randomness or uncertainty. This course provides an in-depth survey of models that can be used to analyze a wide variety of stochastic processes. The focus includes quantitative and theoretical analysis of such models as well as practical issues using such models to represent real problems. This course assumes some prior knowledge of probability and basic stochastic models (like Markov chains).

Class Offered: Fall semester

Prerequisite: OR 542 (Stochastic Models), or STAT 544 (Applied Probability), or permission of instructor

Textbook: S. Ross, Introduction to Probability Models, 11th ed.

Syllabi from past courses

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