SEEM 3570 / ESTR 3508: Stochastic Models

Spring, 2015






General Information

  • Lecture Time and Location: Tuesdays 14:30 - 17:15, in LSK LT 2 (Lee Shau Kee Bldg LT2)
  • Tutorial Times and Locations: Mondays 18:30 - 19:15 and Wednesdays 18:30 - 19:15,
                                                   in ES Lab (ERB 602) for Jan 12, 14, 19, 21, 26, 28, Feb 2 and 4;
                                                   in ERB 407 for other dates.

Teaching Staff

  • Instructor:
    • Yong-Hong KUO  
      • Office: CYT 1150
      • Office hours: Wednesday 15:00 to 17:00 or by appointment
      • Tel: 3943 9592
      • Email: yhkuo at
  • Teaching Assistants:
    • Shumin MA
      • Office: ERB 615
      • Tel: 3943 8498
      • Email: smma at
    • Weijie WU
      • Office: ERB 905
      • Tel: 3943 4241
      • Email: wwu at
    • Man Chung YUE
      • Office: ERB 905
      • Tel: 3943 4241
      • Email: mcyue at


Course Description

In many real-life processes, there is uncertainty so that deterministic models fail to represent the actual systems. Such processes are known as stochastic processes or random processes. In order to build realistic models, stochastic models have been developed to capture uncertainty in systems. At the beginning of this course we will study various regression models. We will focus on the problems of estimation and statistical inference when developing regression models. The regression models developed will be helpful for understanding the relationships among variables, prediction and forecasting, and decision making. Then, we will cover several popular and well-studied stochastic processes: Markov chains, the Poisson process and continuous-time Markov chains. Throughout the course, we will use real-world examples to illustrate the theories and demonstrate the applications.

Students should have workable knowledge in basic probability and statistics, at the level of SEEM 2430 / ENGG 2430 or equivalent.


  • Assignments (30%)
  • Midterm Examination (30%)
  • Final Examination (40%)

Lectur Notes

Tutorial Notes


  • Damodar N. Gujarati and Dawn C. Porter, Basic Econometrics  (5th edition), McGraw-Hill Education, 2009.
  • Sheldon M. Ross, Introduction to Probability Models (11th Edition), Elsevier Science & Technology, 2009.


  • Richard A. Johnson, Miller & Freund’s Probability and Statistics for Engineers (8th Edition), Pearson Education, 2010
  • Saeed Ghahramani, Fundamentals of Probability with Stochastic Processes (3rd Edition), Pearson Education, 2004
  • Robert S. Pindyck, Daniel L. Rubinfeld, Econometric Models and Economic Forecasts (4th Edition), McGraw–Hill Education, 1997
  • Paul Gerhard Hoel, Sidney C. Port, Charles J. Stone, Introduction to Stochastic Processes, Waveland Press, 1986.
  • An excellent set of handouts written by Prof. Anthony So


Please submit your assignment to Assignment Box: D07, 5/F, ERB.

Late assignment submissions will incur a deduction of 25 marks per day. However, no assignment will be accepted after the solution is posted.

Last Updated: April 25, 2015