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Scutari, Marco (2009) Structure Variability in Bayesian Networks. [Working Paper] WORKING PAPER SERIES, 13/2009 . , PADOVA (Inedito)

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Abstract (english)

The structure of a Bayesian network encodes most of the information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it’s important to study the variability of its network structure, which can be used to compare the performance of different learning algorithms and to measure the strength of any arbitrary subset of arcs.
In this paper we will introduce some descriptive statistics and the corresponding parametric and Monte Carlo tests on the undirected graph underlying the structure of a Bayesian network, modeled as a multivariate Bernoulli random variable.


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EPrint type:Working Paper
Anno di Pubblicazione:September 2009
Key Words:Bayesian network, bootstrap, multivariate Bernoulli distribution, structure learning algorithms.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:7154
Depositato il:15 Sep 2014 13:36
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