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Lecture
Markov Chain: Configuration Sampling
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.
Official source
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