Equilibrium of Markov Chains

Related lectures (32)

Page 2 of 4

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

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

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

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

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

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

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

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

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

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