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Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
Explores convergence results for periodic case reversibility in Markov chains, covering irreducible chains, positive recurrence, reversible processes, and random walks on finite graphs.
Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.
