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MATH7080 PROBABILITY AND STOCHASTIC PROCESSES (3,3,0) Syllabus Revised by: Prerequisite:

By S. N. Chiu, C. Rau and C. S. Tong


Postgraduate standing or Consent of instructor Stochastic (or random) processes, and therefore probability theory in its modern (measure theoretic) form, have found wide application in the natural sciences, engineering, as well as in insurance and finance. The unifying feature of stochastic processes is that, unlike a random sample of increasing size, its observations depend on an index that very often represents time, and so there exists a dependence structure between successive observations. The course gives a solid introduction to the most important classes of processes, namely Markov processes and martingales. Somewhat more attention will be given to the discrete-time versions of these processes, which form the basis for analysis of processes which use a continuous time index. Throughout the course, a variety of problems which arise in the mentioned fields of applications will be solved. The learnin

Topics: D. Williams, Probability with Martingales
Year: 2011
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