STOCHASTIC MODELS AND SIMULATION.

Catalog description

A study of processes, whose outcomes are governed by chance, through a combination of lectures and computer lab sessions. Experiments include random number generation, coin tossing and other games of chance, random walks, Markov Chains, Poisson processes, birth-death processes, branching processes, and Brownian Motion. A foundation for modeling random phenomena in sciences, engineering and business. Prerequisite: C or better in MATH 2326 and knowledge of basic probability ( MATH 3313 / STATS 3313 or MATH 3351/BIOL 3351 or equivalent), or consent of instructor, or student group.