This is an introduction to stochastic processes without any measure theory requirements (the little measure theory we use will be covered in lecture.) The main focus of the course will be Markov chains (discrete and continuous time) including queuing and branching processes. The course covers theoretical properties, how to design chains, and how to simulate them on computers. Other topics include martingales; Brownian motion and stochastic integration. No prior programming knowledge will be assumed, and although any programming language (C, R, Turbo Pascal, Logo, whatever) can be used in the course for simulation we will cover the basics of programming in MATLAB.
There will be weekly assignments. Assignments will be roughly 2/3 problem solving, and 1/3 computer work. There will also be three in class quizzes and a final exam as well.
Math 104 and Math 135 or the equivalent. If you are not sure that your background in linear algebra or probability is sufficient, please come see me.
Introduction to Stochastic Processes, by Greg Lawler (Chapman & Hall/CRC).
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