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Unbiased Markov chain Monte Carlo for intractable target distributions

Abstract:

Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as the pseudo-marginal method for latent variable models and the exchange algorithm for a class of undirected graphical models. As with any MCMC algorithm, the resulting estimators ar...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1214/20-EJS1727

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More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0002-0821-4607
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
Institute of Mathematical Statistics
Journal:
Electronic Journal of Statistics More from this journal
Volume:
14
Issue:
2
Pages:
2842-2891
Publication date:
2020-08-07
Acceptance date:
2020-06-12
DOI:
EISSN:
1935-7524
ISSN:
1935-7524
Language:
English
Keywords:
Pubs id:
1112111
Local pid:
pubs:1112111
Deposit date:
2020-06-14

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