Markov Chains and Algorithm Applications

Related lectures (32)

Page 2 of 4

Explains the concept of generators of groups and their mathematical properties.

Delves into mixing time changes in nilflows, emphasizing the delicate nature of mixing and its dependence on singularities.

Covers the generation of primordial magnetic fields and their backreaction effects in cosmology.

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

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

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

Covers hitting probabilities in Markov chains with disjoint subsets, the function h(i), theorems, proofs, and expected time to hit calculations.

Explores martingales, Brownian movement, mean numbers, and generation hypotheses.

Explores low discrepancy sequences and their significance in stochastic simulation and numerical methods.

Explores finitely generated abelian groups, focusing on their structure and isomorphism theorems.