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A stable manifold MCMC method for high dimensions

Beskos, A; (2014) A stable manifold MCMC method for high dimensions. Statistics and Probability Letters , 90 (1) pp. 46-52. 10.1016/j.spl.2014.03.016. Green open access

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Abstract

We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that their mixing time is robust to mesh refinement. © 2014 Elsevier B.V.

Type: Article
Title: A stable manifold MCMC method for high dimensions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.spl.2014.03.016
Publisher version: http://dx.doi.org/10.1016/j.spl.2014.03.016
Language: English
Additional information: © 2014 Elsevier B.V. This manuscript version is made available under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0.
Keywords: Cameron-Martin space, Infinite dimensions, Manifold MCMC, Metropolis-adjusted langevin algorithm
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1425839
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