MTH (0144) 699 - Introduction To Simulation And Stochastic Models

Credits: 3.00

Students will learn stochastic modeling and simulation using modern computer programming languages. Topics include: modeling real world problems with uncertainty; simulating geometric Brownian motion, the Poisson processes in multiple dimensions, and the Gillespie method for simulating finite state Markov Chains; and others at the discretion of the instructor.

Free Note: Admission to a graduate program of study required

Course Learning Goals:

Students will learn how and when to create a stochastic model.
Students will learn how and why a particular distribution is appropriate for modeling different phenomena.
Students will implement MCMC simulations using a modern programming language.
Students will implement the Gillespie method for finite state Markov Chains.
Students will analyze results from simulations.