Markov Chains, Discrete Stochastic Processes and Applications

9 units (3-0-6)  |  second term
Prerequisites: ACM/EE/IDS 116 or equivalent.
Stable laws, Markov chains, classification of states, ergodicity, von Neumann ergodic theorem, mixing rate, stationary/equilibrium distributions and convergence of Markov chains, Markov chain Monte Carlo and its applications to scientific computing, Metropolis Hastings algorithm, coupling from the past, martingale theory and discrete time martingales, rare events, law of large deviations, Chernoff bounds.
Instructor: Owhadi

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