MATH 447 Probability Models

3 Credit(s)

This is an introductory course on probability models with a strong emphasis on stochastic processes. The aim is to enable students to approach real-world phenomena probabilistically and build effective models. The course emphasizes models and their applications over the rigorous theoretical framework behind them, yet critical theory that is important for understanding the material is also covered. Topics include: discrete Markov chains, continuous-time Markov chains, Poisson processes, renewal theory, Brownian motion and martingales. Optional topics: queuing theory, reliability theory, and random sampling techniques. Applications to biology, physics, computer science, economics, and engineering will be presented.

Enrollment Requirements:
Prerequisite: MATH 345