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.
Syllabi from past courses
(fall 2018 and later;
spring 2018 and earlier)
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