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Stochastic Processes
MATH 233
Spring 2024
Section: 01  

This course provides a rigorous introduction to the theory of stochastic processes. Topics include a review of basic concepts of probability theory (probability spaces, random variables, expectation), Markov chains, Poisson processes, random walks, and Brownian motion. In tandem, the workshop section taught by the Hazel Quantitative Analysis Center (QAC) will provide practical skills in R programming. These workshops are geared towards novices in programing and will detail ways to computationally tackle more complex, real-life stochastic processes. Students entering the course should have completed MATH231, and be comfortable with multivariable calculus and linear algebra.
Credit: 1.5 Gen Ed Area Dept: NSM MATH
Course Format: LectureGrading Mode: Graded
Level: UGRD Prerequisites: MATH231
Fulfills a Major Requirement for: None
Past Enrollment Probability: 90% or above

Last Updated on JUN-16-2024
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