STAT246: Probability Theory with Markov Chains
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): STAT 203. Enrollment is restricted to graduate students.
5 credits
| Year | Fall | Winter | Spring | Summer |
|---|---|---|---|---|
| 2024-25 |
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| 2022-23 |
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