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Locally adaptive Monte Carlo methods

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Advanced Monte Carlo Methods for Complex Inference Problems

Co-authors: Christophe Andrieu (University of Bristol), Arnaud Doucet (University of Oxford)

In various situations of interest, natural implementations of Monte Carlo algorithms such as Markov chain Monte Carlo and sequential Monte Carlo can perform poorly due to uneven performance in different parts of the space in which they operate. For example, in Markov chain Monte Carlo a Markov kernel may behave increasingly poorly in the tails of the target distribution of interest and in sequential Monte Carlo the quality of associated estimates may plummet if too few particles are used at a particular time. We overview a particular strategy, local adaptation, that seeks to overcome some of these phenomena in practice.

This talk is part of the Isaac Newton Institute Seminar Series series.

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