This paper proposes and discusses the use of composite marginal like-
lihoods for Bayesian inference. This approach allows one to deal with complex
statistical models in the Bayesian framework, when the full likelihood - and thus
the full posterior distribution - is impractical to compute or even analytically un-
known. The procedure is based on a suitable calibration of the composite likelihood
that yields the right asymptotic properties for the posterior probability distribu-
tion. In this respect, an attractive technique is offered for important settings that
at present are not easily tractable from a Bayesian perspective, such as, for in-
stance, multivariate extreme value theory. Simulation studies and an application
to multivariate extremes are analysed in detail.
%0 Journal Article
%1 pauli2011bayesian
%A Pauli, Francesco
%A Racugno, Walter
%A Ventura, Laura
%D 2011
%J Statistica Sinica
%K Bayesian composite_likelihood methods pseudolikelihood statistics
%N 1
%P 149
%T Bayesian composite marginal likelihoods
%U http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=21&num=1&art=6
%V 21
%X This paper proposes and discusses the use of composite marginal like-
lihoods for Bayesian inference. This approach allows one to deal with complex
statistical models in the Bayesian framework, when the full likelihood - and thus
the full posterior distribution - is impractical to compute or even analytically un-
known. The procedure is based on a suitable calibration of the composite likelihood
that yields the right asymptotic properties for the posterior probability distribu-
tion. In this respect, an attractive technique is offered for important settings that
at present are not easily tractable from a Bayesian perspective, such as, for in-
stance, multivariate extreme value theory. Simulation studies and an application
to multivariate extremes are analysed in detail.
@article{pauli2011bayesian,
abstract = {This paper proposes and discusses the use of composite marginal like-
lihoods for Bayesian inference. This approach allows one to deal with complex
statistical models in the Bayesian framework, when the full likelihood - and thus
the full posterior distribution - is impractical to compute or even analytically un-
known. The procedure is based on a suitable calibration of the composite likelihood
that yields the right asymptotic properties for the posterior probability distribu-
tion. In this respect, an attractive technique is offered for important settings that
at present are not easily tractable from a Bayesian perspective, such as, for in-
stance, multivariate extreme value theory. Simulation studies and an application
to multivariate extremes are analysed in detail.
},
added-at = {2013-05-15T06:45:31.000+0200},
author = {Pauli, Francesco and Racugno, Walter and Ventura, Laura},
biburl = {https://www.bibsonomy.org/bibtex/2a483311b5467897caa33c97f2a5d58de/peter.ralph},
interhash = {5994fab80960f21c5d87a2df448246d2},
intrahash = {a483311b5467897caa33c97f2a5d58de},
journal = {Statistica Sinica},
keywords = {Bayesian composite_likelihood methods pseudolikelihood statistics},
number = 1,
pages = 149,
timestamp = {2013-05-15T07:15:11.000+0200},
title = {Bayesian composite marginal likelihoods},
url = {http://www3.stat.sinica.edu.tw/statistica/password.asp?vol=21&num=1&art=6},
volume = 21,
year = 2011
}