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Aug 26, 2024
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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: Entirely 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.
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