Introduction to Probability Models

Catalog description

Introduces probability modeling and stochastic processes and shows how probability theory can be applied in fields such as engineering, computer science, management science, physical sciences and operations research. Topics include Sample Space, Events, Independence, Discrete and Continuous Random Variables, Conditional Probabilities, Conditional Expectations, Markov Chains, Poisson Process, Simulations. Course overlaps with: TSTAT 345; MATH 394/STAT 394; and STMATH 392. Prerequisite: a minimum grade of 2.0 in either TMATH 208 or TMATH 308. Offered: Sp.