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Scalable Metropolis-Hastings for exact Bayesian inference with large datasets

Abstract:

Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods such as Metropolis-Hastings is too computationally intensive to handle large datasets, since the cost per step usually scales like O(n) in the number of data points n. We propose the Scalable Metropolis-Hastings (SMH) kernel that only requires processing on average O(1) or even O(1/n−−√) data points per step. This scheme is based on a combination of factorized acceptance probabilities, procedures for fast simulation of Be...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
St Peters College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-0821-4607
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
Journal of Machine Learning Research Publisher's website
Volume:
97
Pages:
1351-1360
Publication date:
2019-06-13
Acceptance date:
2019-04-22
ISSN:
2640-3498
Pubs id:
pubs:969289
URN:
uri:82559e39-0ef7-4a8d-b672-532310cf0f99
UUID:
uuid:82559e39-0ef7-4a8d-b672-532310cf0f99
Local pid:
pubs:969289

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