The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Decomposable graphical Gaussian model determination
Abstract
We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the model. Model determination is carried out by implementing a reversible jump MCMC sampler. In particular, the dimension-changing move we propose involves adding or dropping an edge from the graph. We characterise the set of moves which preserve the decomposability of the graph, giving a fast algorithm for maintaining the junction tree representation of the graph at each sweep. As state variable, we propose to use the incomplete variance-covariance matrix, containing only the elements for which the correspondi...
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Cites
General methods for monitoring convergence of iterative simulations
Brooks, S. P., & Gelman, A. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474787 · Cited by 6,045
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
GREEN, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,930
Graphical Models in Applied Multivariate Statistics
Goodall, C. R. (1991) · Technometrics · doi:10.1080/00401706.1991.10484880 · Cited by 1,849
Conditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,501
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476894 · Cited by 1,205
Bayesian Graphical Models for Discrete Data
Madigan, D., York, J., & Allard, D. (1995) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1403615 · Cited by 1,090
Hyper Markov Laws in the Statistical Analysis of Decomposable Graphical Models
Dawid, A. P., & Lauritzen, S. L. (1993) · The Annals of Statistics · doi:10.1214/aos/1176349260 · Cited by 479
Geiger, D., & Heckerman, D. (1994) · Uncertainty Proceedings 1994 · doi:10.1016/b978-1-55860-332-5.50035-3 · Cited by 426
Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
Dellaportas, P. (1999) · Biometrika · doi:10.1093/biomet/86.3.615 · Cited by 163
Cited by
The Practical Implementation of Bayesian Model Selection
Chipman, H., George, E. I., & McCulloch, R. E. (2001) · Institute of Mathematical Statistics Lecture Notes - Monograph Series · doi:10.1214/lnms/1215540964 · Cited by 416
Bayesian methods in bioinformatics and computational systems biology
Wilkinson, D. J. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbm007 · Cited by 230
Metadata
- Authors
- Giudici, P.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-12-01
- Volume
- 86
- Issue
- 4
- Pages
- 785-801
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2095067466
- Cited by
- 263
- References
- 9
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- closed
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