Stochastic Models and their Applications.

Catalog description

This course is structured into modules, each of which describes a real-world problem that can be solved using stochastic modeling techniques, including discrete-time Markov chains, stochastic arrival processes, continuous-time Markov chains and Brownian motion. The course focuses on identifying the sources of randomness, the interactions between them, and the choice of parameters for the specified model. Students in the course will use general programming languages to create simulations as well as specialized simulation software.