Monte Carlo Markov Chains

In course

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Description

This lecture covers the concept of Monte Carlo Markov Chains, focusing on iterative algorithms for sampling trial configurations and accepting states based on probabilities. It also discusses Metropolis-Hastings and Gibbs sampling methods, emphasizing detailed balance and convergence criteria.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_6sqm37sd

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Bayesian statistics, Bayesian inference

Data collection: Experiment

Mathematics

Analysis: Differential anaysis

Chemical engineering

Materials science: Concrete

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