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Clone MCMC: Parallel high-dimensional Gaussian gibbs sampling

Abstract:
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution. Similarly to Hogwild methods, our approach does not target the original Gaussian distribution of interest, but an approximation to it. Contrary to Hogwild methods, a single parameter allows us to trade bias for variance. We show empirically that our method is very flexible and performs well compared to Hogwild-type algorithms.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
Curran Associates
Host title:
Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017)
Journal:
Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017) More from this journal
Volume:
30
Pages:
5021-5029
Publication date:
2018-06-01
Acceptance date:
2017-09-04
ISSN:
1049-5258
ISBN:
9781510860964
Pubs id:
pubs:853892
UUID:
uuid:a93219d0-033d-44f5-8320-e037a1e3d217
Local pid:
pubs:853892
Source identifiers:
853892
Deposit date:
2018-06-14

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