Markov Chain Monte Carlo

Related lectures (24)

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Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

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

Covers the theory and applications of Markov chains, focusing on key concepts and properties.

Explores the composition of applications in mathematics and the importance of understanding their properties.

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

Explores mapping functions, surjections, injective and surjective functions, and bijective functions.

Covers the estimation of relaxation time in chains and the importance of sample sizes.

Covers topics in probability theory, focusing on uniform integrability and convergence theorems.

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

Explores stratified sampling, dividing a population into subgroups for more accurate sampling.