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 is both on quantitative analysis of such models and practical issues using such models to represent real problems. This course assumes some prior knowledge of probability and basic stochastic models (like Markov chains). The pre-requisite is OR 542 (Stochastic Models), or STAT 544 (Applied Probability), or permission of the instructor.
Class Hours: Fall 2010, Thu 4:30 - 7:10 pm, Robinson Hall A, room 105
Prerequisite: OR 542 (Stochastic Models), or STAT 544 (Applied Probability), or permission of instructor
Instructor: John Shortle, , 703-993-3571, Engineering Building, rm 2210
Office Hours (Fall 2010): Wed 3-4pm, Thu 3:30-4:30 pm
Textbook: S. Ross, Introduction to Probability Models, 10th ed.
General Course Information
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