Includes probabilistic and statistical analysis of random processes, continuous-time Markov chains, hidden Markov models, point processes, Markov random fields, spatial and spatio-temporal processes, and statistical modeling and inference in stochastic processes. Applications to a variety of fields. Prerequisite(s): course 205A, 205B, or 261, or permission of instructor. A. Kottas
5 Credits
| Year | Fall | Winter | Spring | Summer |
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| 2011-12 |
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| 2009-10 |
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| 2007-08 |
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| 2005-06 |
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