SYS 6005
Stochastic Modeling I
Course Description
Prerequisites
APMA 3100, 3120, or equivalent background in applied probability and statistics
Covers basic stochastic processes with emphasis on model building and probabilistic reasoning. The approach is non-measure theoretic but otherwise rigorous. Topics include a review of elementary probability theory with particular attention to conditional expectations; Markov chains; optimal stopping; renewal theory and the Poisson process; martingales. Applications are considered in reliability theory, inventory theory, and queuing systems. Prerequisite: APMA 3100, 3120, or equivalent background in applied probability and statistics.
Instructors
Tariq Iqbal
Spring 2026
tba
3.0
Rating
4.0
Difficulty
3.70
GPA
- -
Fall 2024
TBA
—
Rating
—
Difficulty
—
GPA
Jie Liu
Fall 2018
—
Rating
—
Difficulty
3.18
GPA
Stephen Patek
Fall 2017
—
Rating
—
Difficulty
3.49
GPA
Enrique Nanez
Fall 2016
—
Rating
—
Difficulty
—
GPA
Alfredo Garcia
Fall 2014
—
Rating
—
Difficulty
3.71
GPA
Randy Cogill
Fall 2011
—
Rating
—
Difficulty
3.49
GPA