Stochastic Simulation: Variance Reduction Techniques

Related lectures (29)

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Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.

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

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

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

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

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

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

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Explores common probability distributions, special distributions, and entropy concepts.

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.