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.
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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 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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