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DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING Course ECE 30200 - Probabilistic Methods in Electrical Engineering Type of Course Required for the CmpE and EE programs Catalog Description An introductory treatment of probability theory including distribution and density functions, moments, and random variables. Applications of normal and exponential distributions. Estimation of means, variances, correlation, and spectral density functions. Random processes and response of linear systems to random inputs. Credits 3 Contact Hours 3 Prerequisite Courses MA 36300 Corequisite Courses ECE 30100 Textbook R. D. Yates and D. J. Goodman, Probability and Stochastic Systems: A Friendly Introduction for Electrical and Computer Engineers, Wiley, Current Edition. Course Objectives This course is designed to serve as an introduction to experiments, models and probabilities. The following topics will be covered: probability models defined on abstract sets; individual discrete and continuous random variables; pairs of random variables including joint probability functions, conditional probability functions, correlation, and covariance; a brief introduction to stochastic processes with an emphasis on the Poisson process. Course Outcomes Students who successfully complete this course will have demonstrated: 1. An understanding of the basic concepts of probability models defined on abstract sets. (a, e) 2. An understanding of the three axioms of probability. (a, e) 3. An understanding of the law of total probability, Bayes’ theorem, and independence. (a, e) 4. An understanding of counting methods and application to independent trials. (a, e) Department Syllabus ECE – 30200 Page | 1 5. An understanding of probability mass functions and probability density functions. (a, e) 6. An understanding of derived random variables and random variables conditioned on events. (a, e) 7. An understanding of pairs of random variables, including joint probability functions, conditional probability functions, correlation, and covariance. (a, e) 8. An understanding of the basic concepts of stochastic processes. (a, e) 9. An understanding of the Poisson process and its properties. (a, e) Lecture Topics 1. Experiments, models, and probability 2. Discrete random variables 3. Continuous random variables 4. Pairs of random variables 5. Stochastic processes Computer Usage Low Laboratory Experience None Design Experience None Coordinator Chao Chen, Ph.D. Date 31/3/11 Department Syllabus ECE – 30200 Page | 2