Recurrence & Transience

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Related lectures (31)

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Markov Chains: States Classification

Explores the classification of states in Markov chains, emphasizing equivalence classes and periodicity.

Markov Chains and Applications

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

Markov Chains: Transition Densities

Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Fundamental Groups

Explores fundamental groups, homotopy classes, and coverings in connected manifolds.

Markov Chains and Algorithm Applications

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Expected Number of Visits in State

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Recurrence and Transience: Markov Chains

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.