SYS 6005

Stochastic Modeling I

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Course Description

Pre-Requisite(s): 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.


  • Tariq Iqbal

     Rating

    3.00

     Difficulty

    4.00

     GPA

    3.70

     Sections

    2

    Last Taught

    Spring 2026

  • Stephen Patek

     Rating

     Difficulty

     GPA

    3.49

     Sections

    Last Taught

    Fall 2017

  • Randy Cogill

     Rating

     Difficulty

     GPA

    3.49

     Sections

    Last Taught

    Fall 2011

  • Alfredo Garcia

     Rating

     Difficulty

     GPA

    3.71

     Sections

    Last Taught

    Fall 2014

  • Jie Liu

     Rating

     Difficulty

     GPA

    3.18

     Sections

    Last Taught

    Fall 2018

  • Enrique Nanez

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2016

  • - -

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2024