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IE 370 CS - Stochastic Processes & Applic

Campus: Urbana-Champaign

Description:

Introduction to stochastic processes with applications in decision-making under uncertainty. Topics include newsvendor problem, discrete-time Markov chain (including classification of states, stationary distribution, absorbing states), Poisson processes (including time-homogenous, time-nonhomogeneous, thinning Poisson), continuous-time Markov chain (including Markov property, generator matrix, stationary distribution), queuing theory (including M/M/k queue, open Jackson network), and Markov decision processes (including finite-horizon models, infinite-horizon models). Course Information: Prerequisite: IE 300 and IE 310.

Option 1

Number of Required Visit(s): 0

Course Level: Undergraduate

Credit: None

Term(s): Spring


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