Skip Navigation



Biometrika Advance Access published online on August 5, 2007
Biometrika, doi:10.1093/biomet/asm056
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
94/3/647    most recent
asm056v1
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Carvalho, C. M.
Right arrow Articles by West, M.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © 2007 Biometrika Trust

Article

Simulation of hyper-inverse Wishart distributions in graphical models

Carlos M. Carvalho

Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251, U.S.A.

Hélène Massam

Department of Mathematics & Statistics, York University, Toronto M3 J1P3, Canada

Mike West

Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251, U.S.A.

carlos{at}stat.duke.edu

massamh{at}mathstat.yorku.ca

mw{at}stat.duke.edu

Received for publication 1 May 2006. Revision received 1 January 2007.
  Abstract

We introduce and exemplify an efficient method for direct sampling from hyper-inverse Wishart distributions. The method relies very naturally on the use of standard junction-tree representation of graphs, and couples these with matrix results for inverse Wishart distributions. We describe the theory and resulting computational algorithms for both decomposable and nondecomposable graphical models. An example drawn from financial time series demonstrates application in a context where inferences on a structured covariance model are required. We discuss and investigate questions of scalability of the simulation methods to higher-dimensional distributions. The paper concludes with general comments about the approach, including its use in connection with existing Markov chain Monte Carlo methods that deal with uncertainty about the graphical model structure.

Key Words: Gaussian graphical model • Hyper-inverse Wishart • Junction tree • Portfolio analysis • Posterior simulation


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BiometrikaHome page
H. Wang and M. West
Bayesian analysis of matrix normal graphical models
Biometrika, December 1, 2009; 96(4): 821 - 834.
[Abstract] [PDF]

Home page
 BiometrikaHome page
C. M. Carvalho and J. G. Scott
Objective Bayesian model selection in Gaussian graphical models
Biometrika, September 1, 2009; 96(3): 497 - 512.
[Abstract] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.