Markov Chains: Properties and Approximations

Related lectures (32)

Page 3 of 4

Explores classification, communication, and irreducibility in Markov chains.

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

Explores Markov chains' states classification, hitting probabilities, and expected durations in various scenarios.

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

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores the concepts of recurrence and transience in continuous time Markov chains.

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

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

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

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