Lecture

Markov Chain: Configuration Sampling

In course

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Description

This lecture explains the concept of a Markov process and Markov chain in the context of the Metropolis algorithm for generating random configurations in configurational space with correct relative Boltzmann weights. The instructor discusses the importance of starting with a suitable initial configuration, making random changes to the system, and accepting or rejecting new configurations based on their relative Boltzmann weights.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_1gm1351s

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Ontological neighbourhood

Physics

Thermodynamics: Statistical mechanics

Statistics

Data collection: Statistical sampling

Mathematics

Probability theory: Topics in probability theory

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