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MCMC with Metropolis
This lecture covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions. The instructor explains the process of designing a Markov chain to sample from a desired distribution, initializing the chain, updating states, and computing acceptance probabilities. The lecture also delves into the Boltzmann measures and energy differences computation. Practical examples and plots are provided to illustrate the concepts.
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