IER 388 - Stochastic Models
(3 cr.) This course is the second of a two-course sequence that emphasizes modeling and analysis of real-world systems. Continuing from the modeling process introduced in IER 387, this course introduces the stochastic modeling process and many of the classical stochastic models used by systems engineers, operations researchers and management professionals to capture and describe quantitative effects of uncertainty on decision making as part of the Systems Decision Process (SDP). Topics include stochastic life-cycle cost modeling, conditional probability models, basic inference chains, Markov chains, Poisson processes, birth and death processes, counting processes, queuing systems and simulation. Students spend several lessons in a computer lab environment. Prerequisite: IER 387; Every Year, Spring

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