ELEC 4030E-4Z01/COMM 6008E-6001 Random Process 隨機程序 2010 Fall

1. ELEC 4030E-4Z01/COMM 6008E-6001Random Process 隨機程序2010 Fall Instructor: Hsiao-Ping Tsai Email: hptsai@nchu.edu.tw Office: EE711 Phone: 886-4-22851549 ext.711

2. General Course Information • Course Objective The goal of the course is to introduce the subject of probability theory and stochastic processes in engineering • Classroom: EE208 • Class Times: Tue. 2:10pm - 5:00pm • Web site:電機系首頁->課程規章->課程詳述->隨機程序 http://www.ee.nchu.edu.tw/wb_course02.asp?yr=99&cc=2&sn=946

3. General Course Information (con’t) • Instructor: 蔡曉萍 (Hsiao-Ping Tsai ) • Office: EE711 • Phone: (04)22851549 ext. 711 • E-Mail: hptsai@nchu.edu.tw • Office hours: Mon.14：00 ~ 16：00, Wed.10：00 ~ 12：00 • Teaching Assistant: 尤淑佩,尤淑佩 • Office: EE 910 • Phone: (04)22851549 ext. 910 • Email: elaine51666@yahoo.com.tw, evelyn0903@yahoo.com.tw

4. General Course Information (con’t) • Textbook • Sheldon M. Ross, Stochastic Processes 2nd ed. • Wiley, 1996 • ISBN：0471120626 • 國內代理： 歐亞書局 • Reference book • Roy D. Yates and David J. Goodman, Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers 2nd ed. • A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes4th ed.

5. Topics Covered • Basic concepts of probability and random      variables(4 weeks) • Poisson process (2 weeks) • Renewal theory(2 weeks) • Markov chains (4 weeks) • Martingales (2 weeks) • Random walks(2 weeks) • Others: Brownian motion and Other Markov Processes (optional)

6. Topics Covered (con’t) • Basic concepts of probability and random      variables • Random Variable • Probability and Expectations • Probability Inequalities • Poisson Processes • Introduction • Properties • Non-homogeneous Poisson Processes • Compound Poisson Processes • Poisson Arrival See Time Average (PASTA)

7. Topics Covered (con’t) • Renewal Processes • Introduction • Limit Theorems • Key Renewal Theorems • Renewal Reward Processes • Delayed Renewal Processes • Regenerative Processes • Discrete-Time Markov Chains • Introduction • Classification of States • Markov Reward Processes • Time- Reversible Markov Chains • Semi-Markov Chains

8. Topics Covered (con’t) • Martingales • Introduction • Martingals • Stopping Times • Martingale convergence Theorem • Azuma’s Inequality • Random walks • Introduction • Duality in Random Walks • Remarks Concerning Exchangeable Random Walks • G/G/1 Queues and Ruin Problems • Blackwell’s Theorem

9. Grading • Exam I: 20% (10/12) • Exam II: 20% (11/16) • Exam III: 20% (12/21) • Final Exam: 20% (1/18) • Homework: 20%

10. Policies • Late Policy: A homework must be turned in by the midnight of its due day • 5% of points will be deducted for each working day if a homework is turned in late. • A homework assignment will be counted as a Zero scoreonce its solutions are announced. • Attendance Policy: Students are obligated to present in the class. If you cannot present in the class, please ask for leave in advance. • If a student is absent from class more than 3 times, he/she might lose the chance of the grade adjustment at the end of the semester. • Honesty Policy: Students are allowed to discuss problems with their classmates (or me), but they must not blatantly copy others' solutions. • A copying homework is graded zero point. • Assignment Submission: Students should submit their assignments through the ecampus system or to TA.