Chain Graph Models to Elicit the Structure of a Bayesian Network
Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the netw...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/749150 |
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| _version_ | 1832551128804360192 |
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| author | Federico M. Stefanini |
| author_facet | Federico M. Stefanini |
| author_sort | Federico M. Stefanini |
| collection | DOAJ |
| description | Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way
while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures
by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described.
The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated. Finally, a few seminal contributions from the literature are reformulated in terms of structural features. |
| format | Article |
| id | doaj-art-95b6208d70634d65ae5c9dd7ccf2cf3f |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-95b6208d70634d65ae5c9dd7ccf2cf3f2025-02-03T06:04:55ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/749150749150Chain Graph Models to Elicit the Structure of a Bayesian NetworkFederico M. Stefanini0Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, Università degli Studi di Firenze, Viale Morgagni 59, 50134 Firenze, ItalyBayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described. The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated. Finally, a few seminal contributions from the literature are reformulated in terms of structural features.http://dx.doi.org/10.1155/2014/749150 |
| spellingShingle | Federico M. Stefanini Chain Graph Models to Elicit the Structure of a Bayesian Network The Scientific World Journal |
| title | Chain Graph Models to Elicit the Structure of a Bayesian Network |
| title_full | Chain Graph Models to Elicit the Structure of a Bayesian Network |
| title_fullStr | Chain Graph Models to Elicit the Structure of a Bayesian Network |
| title_full_unstemmed | Chain Graph Models to Elicit the Structure of a Bayesian Network |
| title_short | Chain Graph Models to Elicit the Structure of a Bayesian Network |
| title_sort | chain graph models to elicit the structure of a bayesian network |
| url | http://dx.doi.org/10.1155/2014/749150 |
| work_keys_str_mv | AT federicomstefanini chaingraphmodelstoelicitthestructureofabayesiannetwork |