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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
Version:
Publisher's version

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Authors


Bardenet, R More by this author
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Department:
Hertford College
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Department:
Oxford, MPLS, Statistics
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Funding agency for:
Bardenet, R
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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:
1533-7928
ISSN:
1532-4435
Pubs id:
pubs:686656
URN:
uri:a148cceb-11f6-4e35-b8c7-60883621624f
UUID:
uuid:a148cceb-11f6-4e35-b8c7-60883621624f
Local pid:
pubs:686656

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