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A direct sampler for G‐Wishart variates
Author(s) -
Lenkoski Alex
Publication year - 2013
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.23
Subject(s) - wishart distribution , graphical model , conjugate prior , inference , conditional independence , gaussian , jump , posterior probability , computer science , mathematics , algorithm , econometrics , statistics , artificial intelligence , bayesian probability , multivariate statistics , physics , quantum mechanics
The G‐Wishart distribution is the conjugate prior for precision matrices that encode the conditional independence of a Gaussian graphical model. Although the distribution has received considerable attention, posterior inference has proven computationally challenging, in part owing to the lack of a direct sampler. In this note, we rectify this situation. The existence of a direct sampler offers a host of new possibilities for the use of G‐Wishart variates. We discuss one such development by outlining a new transdimensional model search algorithm—which we term double reversible jump—that leverages this sampler to avoid normalizing constant calculation when comparing graphical models. We conclude with two short studies meant to investigate our algorithm's validity. Copyright © 2013 John Wiley & Sons Ltd