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Record Nr. |
UNINA9910484136303321 |
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Titolo |
Advanced Methodologies for Bayesian Networks : Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / / edited by Joe Suzuki, Maomi Ueno |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (XVIII, 265 p. 102 illus. in color.) |
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Collana |
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Lecture Notes in Artificial Intelligence ; ; 9505 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Algorithms |
Mathematical statistics |
Computers |
Database management |
Application software |
Artificial Intelligence |
Algorithm Analysis and Problem Complexity |
Probability and Statistics in Computer Science |
Computation by Abstract Devices |
Database Management |
Information Systems Applications (incl. Internet) |
Intel·ligència artificial |
Estadística bayesiana |
Congressos |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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Effectiveness of graphical models including modeling. Reasoning, model selection -- Logic-probability relations -- Causality. Applying graphical models in real world settings -- Scalability -- Incremental learning.-Parallelization. |
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Sommario/riassunto |
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This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on. |
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