ISE 5350
Transcript Abbreviation:
Probabil Model OR
Course Description:
Introduces probabilistic modeling techniques in operations research like Markov Chains, Poisson Processes, and Markov Decision Processes. Modeling, theory, and applications are discussed.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 3200 and Stat 3470; or permission of instructor.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 7300.
Course Goals / Objectives:
Students will be able to develop an appropriate probabilistic model from a verbal description of a problem
Students will be able to design and optimize Markov Chains
Students will be able to design and optimize Markov Chains
Students will be able to understand the concepts and issues of stochastic behavior, steady-state behavior, discrete-time versus continuous-time models
Students will be able to appreciate the fundamental strengths and weaknesses of these approaches in comparison to alternative approaches
Students will have an understanding of uncertainty in operations research models
Students will be able to analyze and extract information and perform prescriptive analytics from various types of probabilistic models
Students will be able to analyze and extract information and perform prescriptive analytics from various types of probabilistic models
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Introduction to Probabilistic Models and Basics | 3.0 | 0.0 | 0.0 | 0 |
| Conditional Probability and Conditional Expectation | 4.0 | 0.0 | 0.0 | 0 |
| Poisson Process and Its Variations | 6.0 | 0.0 | 0.0 | 0 |
| Discrete-Time Markov Chain | 12.0 | 0.0 | 0.0 | 0 |
| Continuous-Time Markov Chain | 9.0 | 0.0 | 0.0 | 0 |
| Markov Decision Process | 4.0 | 0.0 | 0.0 | 0 |
| Introduction to Stochastic Programming | 4.0 | 0.0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Midterm Exam | 25% |
| Quizzes | 20% |
| Final | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Introduction to Probability Models, Academic Press, San Diego, California, 2010 | S. M. Ross, | |
| Applied Probability and Stochastic Processes, PWS Publishing Company, 1996 | R. M. Feldman and C. Valdez-Flores, |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info:
Attachments:
(N/A)
Additional Notes or Comments:
(N/A)
Basic Course Overview:
ISE_5350_basic.pdf
(10.1 KB)