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IE 410 - Stochastic Processes and Their Applications

Last offered Fall 2022

Official Description

Modeling and analysis of stochastic processes. Transient and steady-state behavior of continuous-time Markov chains; renewal processes; models of queuing systems (birth-and-death models, embedded-Markov-chain models, queuing networks); reliability models; inventory models. Familiarity with discrete-time Markov chains, Poisson processes, and birth-and-death processes is assumed. Course Information: Same as CS 481. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 310.

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Course Description

Modeling and analysis of stochastic processes. Background on probability models. Transient and steady-state behavior of discrete-time Markov chains. Time-reversible Markov chains, Markov decision processes. Applications of Markov chains. Branching processes. Homogeneous and inhomogeneous Poisson processes. Selected topics. Same as CS 481. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 310.

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Adv Stochastic Process & ApplE30396PKG31500 - 1550 M W  1310 Digital Computer Laboratory Sheldon Howard Jacobson
Adv Stochastic Process & ApplE30396PKG31600 - 1650 M W  1310 Digital Computer Laboratory Sheldon Howard Jacobson
Adv Stochastic Process & ApplG58068PKG41600 - 1650 M W  1310 Digital Computer Laboratory Sheldon Howard Jacobson
Adv Stochastic Process & ApplG58068PKG41500 - 1550 M W  1310 Digital Computer Laboratory Sheldon Howard Jacobson