Modelling Stochastic Communications: Reversible Discrete-Time Markov Chains

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Ergodic Theorem: Proof and Applications

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

Markov Chains and Algo Applications

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.

Stochastic Simulation: Theory of Markov Chains

Covers the theory of Markov chains, focusing on reversible chains and detailed balance.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Asymptotic Behavior of Markov Chains

Explores recurrent states, invariant distributions, convergence to equilibrium, and PageRank algorithm.

Stochastic Models for Communications

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Markov Chains: Ergodic Chains Examples

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

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Markov Chains: Reversibility and Stationary Distribution

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.