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On the hypocoercivity of some PDMP-Monte Carlo algorithms

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  • UserChristophe Andrieu, University of Bristol
  • ClockFriday 18 January 2019, 16:00-17:00
  • HouseMR12.

If you have a question about this talk, please contact Dr Sergio Bacallado.

Monte Carlo methods based on Piecewise Deterministic Markov Processes (PDMP) have recently received some attention. In this talk we discuss (exponential) convergence to equilibrium for a broad sub-class of PDMP -MC, covering Randomized Hamiltonian Monte Carlo, the Zig-Zag process and the Bouncy Particle Sampler as particular cases, establishing hypocoercivity under fairly weak conditions and explicit bounds on the spectral gap in terms of the parameters of the dynamics. This allows us, for example, to discuss dependence of this gap on the dimension of the problem for some classes of target distributions.

arXiv:1808.08592

(joint work with Alain Durmus, Nikolas Nüsken, Julien Roussel)

This talk is part of the Statistics series.

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