Type of Credit: Elective
Credit(s)
Number of Students
This course aims to introduce elementary probability theory and stochastic processes. The approach is heuristic and attempts to develop an intuitive feel for the subject. It will start with basic concepts of probability, and then proceed to cover Markov Chains, the exponential distribution and the Poisson process, Brownian motion, simulation techniques, and some selected topics.
能力項目說明
Upon successful completion students should be able to perform probability models to solve practical problems.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Tentative schedule:
1. 2/20 Introduction
2. 2/27 Conditional expectation, smoothing, simple Wald equation
3. 3/06 Markov Chain: limiting probability (probability estimates example)
4. 3/13 Markov Chain: Pattern, mean transient times; AR(1) as a MC
5. 3/20 Time reversible Markov chains, MCMC and hidden Markov models
6. 3/27 exam 1
7. 4/03 holiday
8. 4/10 Exponential distribution and Poisson processes
9. 4/17 Simulation techniques
10. 4/24 Simulation techniques
11. 5/01 Brownian motion
12. 5/08 Brownian motion
13. 5/15 exam 2
14. 5/22 Selected topics
15. 5/29 Presentation
16. 6/05 Presentation
17 + 18: course-related online learning
Evaluation Criteria/Grading policy:
1. homework + in-class exercise 30%
2. exams 20% + 20% = 40%
3. presentation 30%
References:
1. Introduction to probability models, S. Ross, 13th Ed., Academic Press
2. some online materials