Invariant distribution: properties and examples

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

Explores quantum entropy in Markov chains and Bell states, emphasizing entanglement.

Covers Markov Chain Monte Carlo examples and error estimation methods.

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

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.

Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.

Explores the decomposition of Markov chains into communicating classes and the behavior of long-run averages.

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

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

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.