Continuous-Time Markov Chains: Asymptotic Behavior

Related lectures (32)

Page 3 of 4

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

Covers the classification of states in Markov chains.

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

Covers examples of absorbing Markov chains in discrete time.

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

Introduces structural mechanics concepts like distributed loads, centroids, and equilibrium in 2D and 3D.

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

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

Explores discrete-time Markov chains, emphasizing the concept of first passage time in communication systems.

Covers Markov Chain Monte Carlo examples and error estimation methods.