Positive recurrence: invariant distributions

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Page 3 of 4

Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

Explores examples of stationary distribution in Markov chains, including cyclic random walks and the implications of irreducibility.

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

Explores the asymptotic behavior of continuous-time Markov chains and their convergence properties.

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

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

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

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

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

Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.