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Forward Event-Chain Monte Carlo: Fast sampling by randomness control in irreversible Markov chains

Abstract : Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. However, while applying such schemes to standard statistical models, one generally needs to introduce an additional randomization for sake of correctness. We propose here a new class of Event-Chain Monte Carlo methods that reduces this extra-randomization to a bare minimum. We compare the efficiency of this new methodology to standard PDMC and Monte Carlo methods. Accelerations up to several magnitudes and reduced dimensional scalings are exhibited.
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https://hal.archives-ouvertes.fr/hal-03019911
Contributor : Manon Michel Connect in order to contact the contributor
Submitted on : Monday, November 23, 2020 - 3:54:47 PM
Last modification on : Thursday, April 15, 2021 - 3:31:44 AM

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Manon Michel, Alain Durmus, Stéphane Sénécal. Forward Event-Chain Monte Carlo: Fast sampling by randomness control in irreversible Markov chains. Journal of Computational and Graphical Statistics, Taylor & Francis, 2020, ⟨10.1080/10618600.2020.1750417⟩. ⟨hal-03019911⟩

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