Stationary Distribution in Markov Chains

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Markov Chains: Simulation and Optimization

Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.

Markov Chains: Introduction and Properties

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

Stochastic Processes: Time Reversal

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

Continuous Time Markov Chains

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

Continuous-Time Markov Chains: Asymptotic Behavior

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

Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Invariant Distributions: Markov Chains

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

Ergodic Theory: Markov Chains

Explores ergodic theory in Markov chains, discussing irreducibility and unique stationary distributions.

Stochastic Models for Communications

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

Markov Chains: Ergodic Chains Examples

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