Conference item
Scalable Metropolis-Hastings for exact Bayesian inference with large datasets
- Abstract:
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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
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Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research
- Host title:
- Proceedings of Machine Learning Research
- Journal:
- Proceedings of Machine Learning Research More from this journal
- Volume:
- 97
- Pages:
- 1351-1360
- Publication date:
- 2019-06-13
- Acceptance date:
- 2019-04-22
- ISSN:
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2640-3498
Item Description
- Pubs id:
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pubs:969289
- UUID:
-
uuid:82559e39-0ef7-4a8d-b672-532310cf0f99
- Local pid:
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pubs:969289
- Source identifiers:
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969289
- Deposit date:
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2019-06-26
Terms of use
- Copyright holder:
- Cornish et al
- Copyright date:
- 2019
- Notes:
- © The Author(s) 2019. This paper was presented at the 36th International Conference on Machine Learning (ICML 2019), Long Beach, California, USA, June 2019. The final published version and supplementary materials are available online from Proceedings of Machine Learning Research at: http://proceedings.mlr.press/v97/cornish19a.html
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