Skip to main content
Article thumbnail
Location of Repository

Simulation of hyper-inverse Wishart distributions in graphical models

By Carlos M. Carvalho, Hélène Massam and Mike West

Abstract

We introduce and exemplify an efficient method for direct sampling from hyperinverse 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

Topics: Some key words, Gaussian graphical model, Hyper-inverse Wishart, Junction tree, Portfolio analysis, Posterior
Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.2221
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://biomet.oxfordjournals.o... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.