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Covers Markov Chain Monte Carlo for sampling high-dimensional distributions, discussing challenges, advantages, and applications like the Knapsack Problem and cryptography.
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.
