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Record Nr. |
UNINA9910782592403321 |
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Autore |
Riggelsen Carsten |
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Titolo |
Approximation methods for efficient learning of Bayesian networks [[electronic resource] /] / Carsten Riggelsen |
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Pubbl/distr/stampa |
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Amsterdam ; ; Washington, DC, : IOS Press, c2008 |
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ISBN |
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6611733337 |
1-281-73333-4 |
9786611733339 |
1-60750-298-4 |
600-00-0346-3 |
1-4337-1131-1 |
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Descrizione fisica |
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1 online resource (148 p.) |
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Collana |
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Frontiers in artificial intelligence and applications ; ; v. 168 |
Dissertations in artificial intelligence |
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Disciplina |
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Soggetti |
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Bayesian statistical decision theory |
Machine learning |
Neural networks (Computer science) |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [133]-137). |
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Nota di contenuto |
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Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References |
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Sommario/riassunto |
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This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t |
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