Journal article
On Markov chain Monte Carlo Methods for Tall Data
- Abstract:
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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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Authors
Funding
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Bardenet, R
Grant:
ANR-16-CE23-0003
+ 2020 science fellowship
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Funding agency for:
Bardenet, R
Grant:
ANR-16-CE23-0003
+ Agence nationale de la recherche
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Funding agency for:
Bardenet, R
Grant:
ANR-16-CE23-0003
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Doucet, A
Grant:
EP/K000276/1
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
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Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Journal:
- Journal of Machine Learning Research Journal website
- Volume:
- 18
- Issue:
- 47
- Pages:
- 1-43
- Publication date:
- 2017-05-01
- Acceptance date:
- 2017-03-08
- EISSN:
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1533-7928
- ISSN:
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1532-4435
- Source identifiers:
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686656
Item Description
- Pubs id:
-
pubs:686656
- UUID:
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uuid:a148cceb-11f6-4e35-b8c7-60883621624f
- Local pid:
- pubs:686656
- Deposit date:
- 2017-03-22
Terms of use
- Copyright holder:
- © 2017 Doucet, et al
- Copyright date:
- 2017
- Notes:
- This is an open access article published under a creative commons license: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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