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Stochastic Processes

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Amherst College

A stochastic process is a collection of random variables used to model the evolution of a system over time.  Unlike deterministic systems, stochastic processes involve an element of randomness or uncertainty. Examples include stock market fluctuations, audio signals, EEG recordings, and random movement such as Brownian motion and random walks. Topics will include Markov chains, martingales, Brownian motion, and stochastic integration, including Ito’s formula. Four class hours per week, with weekly in-class computer labs.

Requisite: MATH 360 or consent of instructor. Limited to 24 students. Spring semester.  Professor Leise.

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Permission is required for interchange registration during the add/drop period only.
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Course Sections

Stochastic Processes
Sect # Credits Instructor(s) Instructor Email Meeting Times Location
01 4.0 Tanya Leise MWF 09:00AM-09:50AM; TH 09:00AM-09:50AM SMUD 205; SMUD 205