STAT 675 - Advanced Stochastic Processes and Simulation

Units: 2
Theory of stochastic models, Markov chains: classification, limiting behavior, Continuous-time Markov processes. Poisson, birth-death, Simulations of processes and probability modeling. Monte Carlo integration, Gibbs sampling. Use of statistical software. May include: additional limit theorems, queues, renewal theory, applications.

Prerequisites: STAT 620.
Possible Instructional Methods: On-ground.
Grading: A-F grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply probability models for uncertainty, stochastic processes, and distribution theory.
Understand and be able to derive, or program simulation of, basic results of probability theory.
Formulate and model probability problems.
Understand the theory, concepts, and terminology of stochastic processes at a level that supports lifelong learning of appropriate methodologies.