Lecture

Monte Carlo Markov Chains

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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