SYS 3060
Stochastic Decision Models
Course Description
Prerequisites
APMA 3100 or MATH 3100
This is an introductory course on modeling probabilistic systems. The emphasis will be on model formulation and probabilistic analysis. Topics to be covered include general stochastic processes, discrete and continuous time Markov chains, the Poisson Process, Non-Stationary Poisson Processes, Markov Decision Processes, Queueing Theory, and other selected topics. Prerequisite: APMA 3100 or MATH 3100.
Instructors
Daniel Leon
Spring 2026
mowe 12:30pm - 1:45pm
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Rating
—
Difficulty
—
GPA
Robert Riggs
Spring 2025
MoWe 12:30pm - 1:45pm
3.7
Rating
3.5
Difficulty
3.75
GPA
Aram Bahrini
Spring 2022
MoWe 2:00pm - 3:15pm
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Rating
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Difficulty
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GPA
Roman Krzysztofowicz
Spring 2020
2.2
Rating
4.4
Difficulty
3.09
GPA
Quanquan Gu
Spring 2016
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Rating
—
Difficulty
3.31
GPA