Modelling Stochastic Communications: Reversible Discrete-Time Markov Chains

Related lectures (32)

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

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

Covers reversible discrete-time Markov chains in communication models.

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

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

Covers the definitions and state probabilities of continuous-time Markov chains.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

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

Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.

Covers stochastic models for communications, focusing on discrete-time Markov chains.