IE 516: Applied Stochastic Processes
Textbook(s):
Introduction to Probability Models by Sheldon M. Ross, Eighth Edition, Academic Press.
Course Topics:
- Introduction to Probability Theory
- Random Variables
- Conditional Probability and Conditional Expectation
- Markov Chains
- The Exponential Distribution and the Poisson Process
- Continuous - Time Markov Chains
- Renewal Theory and its Applications
Prerequisite Topics:
A formal undergraduate course in engineering probability (IE 322 or STAT 318) is listed as the prerequisite course. Knowledge of the following topics will also satisfy the prerequisite.
- Probability (Sample Spaces, Probability, Conditional Joint and Marginal Probabilities, Baye's Theorem)
- Random Variables and Probability Distributions (Conditional, Marginal and Joint Distributions)
- Expected Value, Variance and Covariance
- Discrete Probability Distributions (Uniform, Binomial, Geometric, Hypergeometric, Negative Binomial, Poisson)
- Continuous Probability Distributions (Uniform, Exponential, Normal, Gamma)
- Functions of Random Variables (Transformation)
Contact: T. Yao