1,721,023 research outputs found
Una procedura di campionamento per importanza per l'inferenza bayesiana in modelli grafici non scomponibili
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On the invariance of conditioning procedures for the specification of prior distributions for nested DAG models
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Comportamento asintotico delle stime di massima verosimiglianza per modelli grafici gaussiani
No full textHyper inverse Wishart distribution for non-decomposable graphs and its application to Bayesian inference for Gaussian graphical models
No full textWhile conjugate Bayesian inference in decomposable Gaussian graphical models is largely solved, the non-decomposable case still poses difficulties concerned with the specification of suitable priors and the evaluation of normalizing constants. In this paper we derive the DY-conjugate prior (Diacouls &Ylvisaker, 1979) for non-decomposable models and show that it can be regarded as a generalization to an arbitrary graph G of the hyper inverse Wishart distribution (Dawid &Lauritzen, 1993). In particular, if G is an incomplete prime graph it constitutes a non-trivial generalization of the inverse Wishart distribution. Inference based on marginal likelihood requires the evaluation of a normalizing constant and we propose an importance sampling algorithm for its computation. Examples of structural learning involving non-decomposable models are given. In order to deal efficiently with the set of all positive definite matrices with non-decomposable zero-pattern we introduce the operation of tr..
Confronto di coefficienti di correlazione in presenza di relazioni di indipendenza condizionata tra variabili
No full textPrior distributions for Gaussian graphical models: a comparison between the directed and undirected case
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