Bayesian Inference For Nondecomposable Graphical Gaussian Models.

Lancaster University

Keywords

Importance sampling, partial correlation coefficient, sampling from conditional Wishart distibution.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Bayesian Inference For Nondecomposable Graphical Gaussian Models. / Dellaportas, Petros; Giudici, Paolo; Roberts, Gareth.
In: Sankhya - Series A, Vol. 65, No. 1, 2003, p. 43-55.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Dellaportas, P, Giudici, P & Roberts, G 2003, 'Bayesian Inference For Nondecomposable Graphical Gaussian Models.', Sankhya - Series A, vol. 65, no. 1, pp. 43-55. <http://sankhya.isical.ac.in/index.html>

APA

Dellaportas, P., Giudici, P., & Roberts, G. (2003). Bayesian Inference For Nondecomposable Graphical Gaussian Models. Sankhya - Series A, 65(1), 43-55. http://sankhya.isical.ac.in/index.html

Vancouver

Dellaportas P, Giudici P, Roberts G. Bayesian Inference For Nondecomposable Graphical Gaussian Models. Sankhya - Series A. 2003;65(1):43-55.

Author

Dellaportas, Petros; ; Giudici, Paolo; ; Roberts, Gareth. / Bayesian Inference For Nondecomposable Graphical Gaussian Models. In: Sankhya - Series A. 2003 ; Vol. 65, No. 1. pp. 43-55.

Bibtex

@article{cdc1b115830e4e1ab4fe58f942d63d56,

title = "Bayesian Inference For Nondecomposable Graphical Gaussian Models.",

abstract = "In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints. As a result, our methodology allows Bayesian model selection within the {\em whole} class of graphical Gaussian models, including nondecomposable ones.",

keywords = "Importance sampling, partial correlation coefficient, sampling from conditional Wishart distibution.",

author = "Petros; Dellaportas and Paolo; Giudici and Gareth Roberts",

year = "2003",

language = "English",

volume = "65",

pages = "43--55",

journal = "Sankhya - Series A",

issn = "0581-572X",

number = "1",

}

RIS

TY - JOUR

T1 - Bayesian Inference For Nondecomposable Graphical Gaussian Models.

AU - Dellaportas, Petros;

AU - Giudici, Paolo;

AU - Roberts, Gareth

PY - 2003

Y1 - 2003

N2 - In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints. As a result, our methodology allows Bayesian model selection within the {\em whole} class of graphical Gaussian models, including nondecomposable ones.

AB - In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, conditional on the graphical constraints. As a result, our methodology allows Bayesian model selection within the {\em whole} class of graphical Gaussian models, including nondecomposable ones.

KW - Importance sampling

KW - partial correlation coefficient

KW - sampling from conditional Wishart distibution.

M3 - Journal article

VL - 65

SP - 43

EP - 55

JO - Sankhya - Series A

JF - Sankhya - Series A

SN - 0581-572X

IS - 1

ER -

Research

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Keywords