Sampling from Rough Energy Landscapes

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Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

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Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

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Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Markov Chain Monte Carlo

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Markov Chains: Applications and Sampling Methods

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Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.

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Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Markov Chain Monte Carlo: Sampling and Convergence

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