STAT 551 Applied Random Processes

Introduction to random processes, also called stochastic processes. Topics may include conditional probability, random variables, theory and simulation of finite Markov chains, Gibbs sampler, Metropolis-Hastings algorithm, theory and simulation of Poisson processes. Use of computer software such as R or Python.

Credits

4

Prerequisite

STAT 315 and MATH 228 or permission of department chair.

General Education

Offered

• Fall

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