# AMS261: Probability Theory with Markov Chains

*****COURSES ARE SUBJECT TO CHANGE*****

Introduction to probability theory: probability spaces, expectation as Lebesgue integral, characteristic functions, modes of convergence, conditional probability and expectation, discrete-state Markov chains, stationary distributions, limit theorems, ergodic theorem, continuous-state Markov chains, applications to Markov chain Monte Carlo methods. Prerequisite(s): course 205B or by permission of instructor. Enrollment restricted to graduate students. A. Kottas

5 Credits

YearFallWinterSpringSummer
2017-18
2015-16
2013-14
2010-11
2007-08
2006-07
2005-06
2004-05

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