IENG.3020 Stochastic Modeling and Analysis

Id: 041732 Credits: 3-3

Description

An introduction to the theory, algorithms, approximations, and applications of stochastic processes. Topics studied include Markov chain and continuous and continuous time Markov process models and applications, renewal processes, Brownian Motion, analytical and numerical approximation methods, Markov decision processes. Application areas include inventory control, reliability, queuing, and decision analysis.

Prerequisites

MATH.3860 Probability and Statistics.

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Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.