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IE 370 - Stochastic Processes & Applic

Spring 2021

Official 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.

Related Faculty

Documents

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Stochastic Processes & ApplicSP66991OLC31700 - 1820 T R    Alexander Stolyar