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A Bayesian approach to multicanonical Markov chain Monte Carlo

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If you have a question about this talk, please contact Fredrik Lindsten.

The Markov chain Monte Carlo method is one of the most important tools for approximate inference. However, the canonical ensemble, known as the posterior distribution in Bayesian inference and the Boltzmann distribution in physics, is in many cases difficult to sample from due to slow convergence and poor mixing of the Markov chain. The so-called multicanonical ensemble can potentially alleviate this problem by constructing a new distribution that performs a random walk between different values of the energy function. Estimates for posterior averages and partition functions can then be obtained by reweighting. The main challenge in applying the multicanonical ensemble is that one has to solve an inference problem on the density of energy under the prior. In this talk I will present a Bayesian approach to solve this problem using a Gaussian Process prior. I will present preliminary results on spin models and discuss applications in protein folding.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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