liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Speeding Up MCMC by Efficient Data Subsampling
Univ New South Wales, Australia.
Univ New South Wales, Australia.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
Univ Sydney, Australia.
2019 (English)In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274X, Vol. 114, no 526, p. 831-843Article in journal (Refereed) Published
Abstract [en]

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed posterior, for which we derive the asymptotic error with respect to n and m, respectively. We propose a practical estimator of the error and show that the error is negligible even for a very small m in our applications. We demonstrate that subsampling MCMC is substantially more efficient than standard MCMC in terms of sampling efficiency for a given computational budget, and that it outperforms other subsampling methods for MCMC proposed in the literature. Supplementary materials for this article are available online.

Place, publisher, year, edition, pages
AMER STATISTICAL ASSOC , 2019. Vol. 114, no 526, p. 831-843
Keywords [en]
Bayesian inference; Big Data; Block pseudo-marginal; Correlated pseudo-marginal; Estimated likelihood; Survey sampling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159005DOI: 10.1080/01621459.2018.1448827ISI: 000472559400027OAI: oai:DiVA.org:liu-159005DiVA, id: diva2:1338106
Note

Funding Agencies|Australian Research Council Center of Excellence grant [CE140100049]; VINNOVA grant [2010-02635]; Swedish Foundation for Strategic Research [RIT 15-0097]; Business School Pilot Research grant

Available from: 2019-07-19 Created: 2019-07-19 Last updated: 2019-07-19

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Villani, Mattias
By organisation
The Division of Statistics and Machine LearningFaculty of Arts and Sciences
In the same journal
Journal of the American Statistical Association
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf