*****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.
5 Credits
Year | Fall | Winter | Spring | Summer |
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2017-18 |
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2015-16 |
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2013-14 |
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2010-11 |
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2007-08 |
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2006-07 |
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2005-06 |
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2004-05 |
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