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SUMMARYThe combination of graphical models and reference analysis represents a powerful tool for Bayesian inference in highly multivariate settings. It is typically difficult to derive reference priors in complex problems. In this paper we present a suitable mixed parameterisation for a discrete decomposable graphical model and derive the corresponding reference prior.
Directed acyclic graphs (DAGs) have become increasingly popular to communicate, model and manage complex systems ef®ciently. 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.
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