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On Markov chain Monte Carlo Methods for Tall Data

Abstract:

Markov chain Monte Carlo methods are often deemed too computationally intensive to be of any practical use for big data applications, and in particular for inference on datasets containing a large number n of individual data points, also known as tall datasets. In scenarios where data are assumed independent, various approaches to scale up the Metropolis- Hastings algorithm in a Bayesian inference context have been recently proposed in machine learning and computational statistics. These appr...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Hertford College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Name:
Medical Research Council
Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
More from this funder
Name:
Agence nationale de la recherche
Funding agency for:
Bardenet, R
Grant:
ANR-16-CE23-0003
More from this funder
Name:
2020 science fellowship
Funding agency for:
Bardenet, R
Grant:
ANR-16-CE23-0003
More from this funder
Name:
Engineering and Physical Sciences Research Council
Funding agency for:
Bardenet, R
Doucet, A
Holmes, C
Grant:
ANR-16-CE23-0003
EP/K000276/1
MC UP A390 1107
Publisher:
Journal of Machine Learning Research
Journal:
Journal of Machine Learning Research More from this journal
Volume:
18
Issue:
47
Pages:
1-43
Publication date:
2017-05-01
Acceptance date:
2017-03-08
EISSN:
1533-7928
ISSN:
1532-4435
Pubs id:
pubs:686656
UUID:
uuid:a148cceb-11f6-4e35-b8c7-60883621624f
Local pid:
pubs:686656
Source identifiers:
686656
Deposit date:
2017-03-22

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