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- Title
Objective Bayesian model selection in Gaussian graphical models.
- Authors
CARVALHO, C. M.; SCOTT, J. G.
- Abstract
This paper presents a default model-selection procedure for Gaussian graphical models that involves two new developments. First, we develop a default version of the hyper-inverse Wishart prior for restricted covariance matrices, called the hyper-inverse Wishart g-prior, and show how it corresponds to the implied fractional prior for selecting a graph using fractional Bayes factors. Second, we apply a class of priors that automatically handles the problem of multiple hypothesis testing. We demonstrate our methods on a variety of simulated examples, concluding with a real example analyzing covariation in mutual-fund returns. These studies reveal that the combined use of a multiplicity-correction prior on graphs and fractional Bayes factors for computing marginal likelihoods yields better performance than existing Bayesian methods.
- Subjects
GRAPHICAL modeling (Statistics); GAUSSIAN processes; BAYESIAN analysis; MATRICES (Mathematics); GRAPHIC methods for multivariate analysis
- Publication
Biometrika, 2009, Vol 96, Issue 3, p497
- ISSN
0006-3444
- Publication type
Article
- DOI
10.1093/biomet/asp017