Model Determination for Directed Acyclic Graphs

G. Consonni; Guido Consonni; V. Leucari; Valentina Leucari

doi:10.1111/1467-9884.00275

Model Determination for Directed Acyclic Graphs

G. Consonni, Guido Consonni, V. Leucari, Valentina Leucari

University of Pavia

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Directed acyclic graphs (DAGs) have become increasingly popular to communicate, model and manage complex systems efficiently. They also represent a major tool in probabilistic expert systems. We address the issue of model determination for DAGs, with respect to a given ordering of the variables, together with the corresponding parameter estimation, and show how this can be done in a simple way using available software for Bayesian data analysis, such as BUGS

Original language	English
Pages (from-to)	243-256
Number of pages	14
Journal	JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN
Volume	50
DOIs	https://doi.org/10.1111/1467-9884.00275
Publication status	Published - 2001

Keywords

Bayesian model selection
Bayesian network

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10.1111/1467-9884.00275

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AU - Consonni, Guido

AU - Leucari, V.

AU - Leucari, Valentina

PY - 2001

Y1 - 2001

N2 - Directed acyclic graphs (DAGs) have become increasingly popular to communicate, model and manage complex systems efficiently. They also represent a major tool in probabilistic expert systems. We address the issue of model determination for DAGs, with respect to a given ordering of the variables, together with the corresponding parameter estimation, and show how this can be done in a simple way using available software for Bayesian data analysis, such as BUGS

AB - Directed acyclic graphs (DAGs) have become increasingly popular to communicate, model and manage complex systems efficiently. They also represent a major tool in probabilistic expert systems. We address the issue of model determination for DAGs, with respect to a given ordering of the variables, together with the corresponding parameter estimation, and show how this can be done in a simple way using available software for Bayesian data analysis, such as BUGS

KW - Bayesian model selection

KW - Bayesian network

KW - Bayesian model selection

KW - Bayesian network

UR - http://hdl.handle.net/10807/12347

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DO - 10.1111/1467-9884.00275

M3 - Article

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VL - 50

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EP - 256

JO - JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN

JF - JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN

ER -

Model Determination for Directed Acyclic Graphs

Abstract

Keywords

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