Department of Mathematics

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MATH 442: Probability Models

Prerequisites & Bulletin Description

Course Objectives

Students will learn to:

  1. Calculate conditional probability, conditional expectation, and conditional variance.
  2. Define Markov Chains and do calculations based on them.
  3. Define Exponential and Poisson Processes and use them in applications.
  4. Define Continuous Time Markov Chains and do calculations based on them.
  5. Define M/M/1 Queues, M/G/1 Queues, and G/M/1 Queues, and use them in applications.

Evaluation of Students

Students will demonstrate their mastery of the course objectives on frequent graded homework assignments, quizzes, exams, and a final exam.

Course Outline

The order in which we work on the following topics may vary.

  1. Conditional Probability
  2. Conditional Expectation
  3. Conditional Variance
  4. Markov Chain
  5. Exponential Process
  6. Poisson Process
  7. Continuous Time Markov Chain
  8. Queuing Theory


Introduction to Probability Models by Sheldon M. Ross.

Submitted by: David Bao
Date: September 13, 2012

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