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Piecewise deterministic Markov processes and efficiency gains through exact subsampling for MCMC

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SIN - Scalable inference; statistical, algorithmic, computational aspects

Markov chain Monte Carlo methods provide an essential tool in statistics for sampling from complex probability distributions. While the standard approach to MCMC involves constructing discrete-time reversible Markov chains whose transition kernel is obtained via the Metropolis- Hastings algorithm, there has been recent interest in alternative schemes based on piecewise deterministic Markov processes (PDMPs). One such approach is based on the Zig-Zag process, introduced in Bierkens and Roberts (2016), which proved to provide a highly scalable sampling scheme for sampling in the big data regime (Bierkens, Fearnhead and Roberts (2016)). In this talk we will present a broad overview of these methods along with some theoretical results.



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

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