The local specification of priors in non-decomposable graphical models does not necessarily yield a proper joint prior for all the parameters of the model. Using results concerning general exponential families with cuts, we derive specific results for the multivariate Gamma distribution (conjugate prior for Poisson counts) and the Wishart distribution (conjugate prior for Gaussian models). These results link the existence of a locally specified joint prior to the solvability of a related marginal problem over the cliques of the graph. Copyright 2007 Board of the Foundation of the Scandinavian Journal of Statistics..
Generalised natural conjugate prior densities: Singular multivariate linear model Jose ́ A. Dı́az-Ga...
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requi...
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The local specification of priors in nondecomposable graphical models does not necessarily yield a p...
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Given a multinomial decomposable graphical model, we identify several alternative parametrizations; ...
AbstractGiven a multinomial decomposable graphical model, we identify several alternative parametriz...
We formulate a novel approach to infer conditional independence models or Markov structure of a mult...
This thesis contributes to the field of Gaussian Graphical Models by exploring either numerically or...
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In this paper we propose a method to calculate the posterior probability of a nondecomposable graphi...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
A centred Gaussian model that is Markov with respect to an undirected graph G is characterised by th...
Generalised natural conjugate prior densities: Singular multivariate linear model Jose ́ A. Dı́az-Ga...
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requi...
AbstractA method for constructing priors is proposed that allows the off-diagonal elements of the co...
The local specification of priors in nondecomposable graphical models does not necessarily yield a p...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian ...
Given a multinomial decomposable graphical model, we identify several alternative parametrizations; ...
AbstractGiven a multinomial decomposable graphical model, we identify several alternative parametriz...
We formulate a novel approach to infer conditional independence models or Markov structure of a mult...
This thesis contributes to the field of Gaussian Graphical Models by exploring either numerically or...
In the framework of graphical models the graphical representation of the association structure is us...
This paper explores the usefulness of the multivariate skew-normal distribution in the context of gr...
In this paper we propose a method to calculate the posterior probability of a nondecomposable graphi...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
A centred Gaussian model that is Markov with respect to an undirected graph G is characterised by th...
Generalised natural conjugate prior densities: Singular multivariate linear model Jose ́ A. Dı́az-Ga...
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requi...
AbstractA method for constructing priors is proposed that allows the off-diagonal elements of the co...