Discrete-Time Markov Chains: Reversible Chains

Related lectures (31)

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Explores classification, communication, and irreducibility in Markov chains.

Covers the basic theory for continuous time Markov chains and discusses communication, hitting probabilities, recurrence, and transience.

Explores the concepts of recurrence and transience in continuous time Markov chains.

Explores time reversal in stationary Markov chains and the concept of detailed balance conditions.

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

Covers reversible discrete-time Markov chains and their concept of reversibility.

Explores challenges in human communication, context importance, compression problems, uncertain functionality, and proof verification.

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Explores reversibility and irreducibility in Markov chains, along with optimal decision-making policies.

Covers reversible discrete-time Markov chains in stochastic communications, explaining stationarity and transition probabilities.