Lectures related to Equilibrium of Markov Chains

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

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

Continuous-Time Markov Chains: Asymptotic Behavior

Covers the behavior of continuous-time Markov chains and their convergence to equilibrium.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains, focusing on key concepts and properties.

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 and Algo Applications

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

Markov Chains: PageRank Algorithm

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

Geometric Ergodicity: Convergence Diagnostics

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Markov Chains: Convergence and Equilibrium

Explores the convergence properties of Markov chains and the computation of long-run mean rewards.

Applied Probability & Stochastic Processes

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

Markov Chains: Introduction and Properties

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

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Markov Chains: Theory and Applications

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

Stochastic Processes: Markov Chains

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

Markov Chains: Reversibility & Convergence

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

Markov Chains: Applications and Coupled Chains

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.

Markov Chains and Algorithm Applications

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

Markov Chains and Applications

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

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Markov Chains: Ergodic Chains Examples

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