Brief course descriptions
This course is for students to achieve the following goals: To develop an ability to model dynamical processes as stochastic processes; To develop an understanding of important qualitative characteristics of stochastic processes; To develop an ability to analyze basic stochastic processes.
Course keywords
高等機率, 隨機模式, 高等統計, 模擬, 馬可夫鏈
Prerequisites
IEEM203000 (Engineering Statistics) or equivalent basic probability course.
Textbook
Introduction to Probability Models, 10th Ed. by Sheldon M. Ross, Academic Press, 2009 (or the newest version).
Course Topics
Chapter 3. Conditional Probability and Conditional Expectation
Chapter 4. Markov Chains
Chapter 5. The Exponential Distribution and the Poisson process
Chapter 6. Continuous-time Markov Chain (CTMC)
Chapter 7. Renewal theory and its applications
Chapter 8. Queueing Theory
