Bayesian analysis of matrix normal graphical models.

Published

Journal Article

We present Bayesian analyses of matrix-variate normal data with conditional independencies induced by graphical model structuring of the characterizing covariance matrix parameters. This framework of matrix normal graphical models includes prior specifications, posterior computation using Markov chain Monte Carlo methods, evaluation of graphical model uncertainty and model structure search. Extensions to matrix-variate time series embed matrix normal graphs in dynamic models. Examples highlight questions of graphical model uncertainty, search and comparison in matrix data contexts. These models may be applied in a number of areas of multivariate analysis, time series and also spatial modelling.

Full Text

Duke Authors

Cited Authors

  • Wang, H; West, M

Published Date

  • December 2009

Published In

Volume / Issue

  • 96 / 4

Start / End Page

  • 821 - 834

PubMed ID

  • 22822246

Pubmed Central ID

  • 22822246

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asp049

Language

  • eng