Markov Chains: Reversibility & Convergence

Related lectures (31)

Page 3 of 4

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Explores reversibility and irreducibility in Markov chains, along with optimal decision-making policies.

Covers stochastic simulation and variance reduction techniques, focusing on Courra variate and auxiliary distribution generation.

Covers Mod.7 on continuous-time Markov chains, focusing on reversible chains and their applications in communication systems.

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

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

Covers the concept of ergodicity in continuous-time stochastic processes and the convergence of statistical properties over time.

Covers continuous-time Markov chains, focusing on reversible chains and their properties.

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

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.