Title	On the hypocoercivity of some PDMP-Monte Carlo algorithms
Creator	Andrieu, Christophe
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-11-15T09:02
Description	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 in terms of the dimension of the problem for some classes of target distributions.
Extent	37.0
Subject	Mathematics; Statistics; Statistical mechanics, structure of matter; Statistical mechanics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Bristol
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2019-05-15
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0378720
URI	http://hdl.handle.net/2429/70164
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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