Markov Chains: State Classification

Related lectures (32)

Page 3 of 4

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

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

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.

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

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

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.

Covers the classification of states in discrete-time Markov chains and explores the concept of periodicity.

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

Covers examples of absorbing chains in discrete-time Markov chains.