| | Standard Course Syllabus | Course Supervisor | Date of Approval |
| | Dept. of Electrical and Computer Engineering | Koksal | Not yet approved by area |
| | 804 | Random Signal Analysis |
| | 2. | CATALOG DESCRIPTION |
| | Probability, random variables, and random vectors for analysis and research in electrical engineering. Distribution functions |
| | and densities, expectation, characteristic functions, functions of random variables, random vectors and sequences, stochastic |
| | convergence. |
| | Quarters of Offering | Credits | | Level | Class Meeting |
| | Au Qtr. | 3 | G | 3 cl. |
| | Course Prerequisites |
| | Prereq: 352 or equiv; Stat 427 or Math 530 or equiv. |
| | 3. | PREREQUISITES BY TOPIC |
| | Linear system theory, undergraduate probability or statistics. |
| | Courses that require this as a direct prerequisite |
| | 802, 803, 805 |
| | 4. | Text(s) and Other Course Materials | Author(s) | Publisher |
| | Probability, Random Variables and Stochastic Processes, 4th | Papoulis and Pillai | McGraw-Hill |
| | Ed., 2002 |
| | ISBN: 0-07-366011-6 |
| | References (supplemental reading) |
| | [1] Probability and Stochastic Processes for Engineers: Helstrom |
| | [2] Probability and Random Processes: Viniotis |
| | 5. | COURSE OBJECTIVES |
| | 1. Students learn the mathematical foundations and tools of probability theory, including probability spaces, univariate and |
| | multivariate distribution and density functions, expectation and conditional expectation, characteristic functions, functions of |
| | random variables, random vectors and sequences, and stochastic convergence. (Criterion 3(a)) |
| | 2. Students learn the basics of estimation theory, including maximum likelihood, least-square estimation, and Bayesian |
| | decision theory. (Criteria 3(a),(e),(k)) |
| | 6. | TOPICS AND (# OF LECTURES) |
| | Preliminaries, Axioms, Probability Spaces (2) |
| | Bayes' Rule and all its component concepts (2) |
| | Random Variables, Distributions and Densities (4) |
| | Conditional and Joint Distributions and Densities (4) |
| | Functions of Random Variables (4) |
| | Expectations (3) |
| | Random Vectors, Covariance Matrices (4) |
| | Maximum Likelihood Parameter Estimation (2) |
| | Least Squares Estimation (1) |
| | Bayesian Decision Theory (1) |
| | Random Sequences, Convergence concepts (3) |
| | 7. | CLASS MEETING PATTERN | (For example, "3cl." means 3 48-min classes per week.) |
| | 3 cl. |
| | Thursday, August 14, 2008 09:22 AM |
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