Simulation of hyper-inverse Wishart distributions in graphical models

Published

Journal Article

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. © 2007 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Carvalho, CM; Massam, H; West, M

Published Date

  • September 14, 2007

Published In

Volume / Issue

  • 94 / 3

Start / End Page

  • 647 - 659

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asm056

Citation Source

  • Scopus