Markov Chains and Applications

Related lectures (32)

Page 1 of 4

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

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

Covers Markov Chain Monte Carlo examples and error estimation methods.

Covers the basics of Markov chains and their algorithmic applications.

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

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

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

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

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

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