ECE 6001
Transcript Abbreviation:
Prob & Rand Varb
Course Description:
Probability, random variables, and random vectors for analysis and research in electrical engineering. Distribution functions, characteristic functions, functions of random variables and vectors, Markov chains.
Course Levels:
Graduate (5000-8000 level)
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: Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 804 or 805.
Course Goals / Objectives:
Learn the mathematical foundations and tools of probability theory
Learn probability spaces, univariate and multivariate distribution and density functions, expectation and conditional expectation, characteristic functions, functions of random variables and vectors, and Markov chains
Learn the basics of estimation theory, including least-square estimation and Bayesian decision theory, and Markov chains
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Preliminaries, Axioms, Probability Spaces | 2.0 | 0.0 | 0 | 0.0 | 0 |
| Bayes' Rule and all its component concepts | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Random Variables, Distributions, and Densities | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Conditional and Joint Distributions and Densities | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Functions of Random Variables | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Expectations | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Random Vectors, Covariance Matrices | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Least Square Estimation | 2.0 | 0.0 | 0 | 0.0 | 0 |
| Bayesian Decision Theory | 2.0 | 0.0 | 0 | 0.0 | 0 |
| Bernoulli Process | 1.0 | 0.0 | 0 | 0.0 | 0 |
| Poisson Process | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Markov Chains | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Weak Law of Large Numbers | 1.0 | 0.0 | 0 | 0.0 | 0 |
| Central Limit Theorem | 1.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 41 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework Assignments | 15% |
| Midterm Exam | 35% |
| Final Exam | 50% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Introduction to Probability | Dimitri P. Bertsekas and John N. Tsitsiklis |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: