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Massa, Maria Sofia (2008) Combining information from Gaussian graphical models. [Tesi di dottorato]

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Abstract (inglese)

Given a joint probability distribution, one can generally find its marginal components. However, it is not straightforward, or even possible, the inverse procedure. In this thesis, we shall study the wide context of combining families of distributions. In particular, we shall consider absolutely continuous distributions with respect to a product measure.
The conditions for compatibility of the marginal families shall be the initial research problem to be investigated. Next, we shall classify two types of combination corresponding to the initial available information.
The methodology previously introduced shall permit to lead the way to study the combination of families of multivariate normal distributions respecting some conditional independence relationships, i.e., Gaussian graphical models.
Examples of combination of Gaussian graphical models and methods to estimate the parameters of the joint family of distributions shall be also provided.
Eventually, we shall perform two simulation studies to assess the proposed methodology.


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Tipo di EPrint:Tesi di dottorato
Relatore:Chiogna, Monica
Correlatore:Lauritzen, Steffen
Dottorato (corsi e scuole):Ciclo 20 > Scuole per il 20simo ciclo > SCIENZE STATISTICHE
Data di deposito della tesi:31 Gennaio 2008
Anno di Pubblicazione:31 Gennaio 2008
Parole chiave (italiano / inglese):Gaussian graphical models, consistent distributions, meta-analysis of graphical models, meta-consistent families, meta-Markov combination, quasi-consistent families, quasi-Markov combination, graphical combination, non-graphical combination.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:788
Depositato il:13 Nov 2008
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