Dec 01, 2023  
2021-2022 Undergraduate Academic Catalog 
2021-2022 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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IE 3820 - Stochastic Processes

4 lecture hours 0 lab hours 4 credits
Course Description
This course continues the modeling approach to problem solving by presenting techniques used to analyze and design systems affected by random variables. Queueing theory, Markov processes, and decision theory are examined. Case studies and computer algorithms are utilized. (prereq: MA 262 , IE 381 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Identify and apply quantitative analysis techniques to engineering problems related to random processes
  • Use quantitative management technique results to analyze alternative solutions and assist in decision making
  • Have an understanding of how these methods impact business and industry
  • Demonstrate systematic problem-solving skills and be able to communicate the process effectively

Prerequisites by Topic
  • Understanding of basic probabilistic principles and calculations
  • Familiarity with common discrete probability distributions
  • An ability to take complex derivatives and limits

Course Topics
  • Introduction to quantitative management
  • Probability for stochastic processes
  • Fundamentals of decision theory
  • Decision theory and utility theory
  • Queueing theory
  • Markov analysis
  • Dynamic programming
  • Review
  • Examinations

Dr. Aaron Armstrong

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