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Efficient Bayesian Inference for Multivariate Probit Models With Sparse Inverse Correlation Matrices

Abstract:

We propose a Bayesian approach for inference in the multivariate probit model, taking into account the association structure between binary observations. We model the association through the correlation matrix of the latent Gaussian variables. Conditional independence is imposed by setting some off-diagonal elements of the inverse correlation matrix to zero and this sparsity structure is modeled using a decomposable graphical model. We propose an efficient Markov chain Monte Carlo algorithm r...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
Journal:
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume:
21
Issue:
3
Pages:
739-757
Publication date:
2012-09-05
DOI:
ISSN:
1061-8600
URN:
uuid:dc284bf0-3c01-44de-9cd2-e2457c857922
Source identifiers:
351034
Local pid:
pubs:351034

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