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Approximation Methods for Efficient Learning of Bayesian Networks.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
IOS Press,
2008.
|
| Colección: | Frontiers in artificial intelligence and applications.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
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| 008 | 130418s2008 ne o 000 0 eng d | ||
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| 019 | |a 1227951224 |a 1264943552 | ||
| 020 | |a 1586038214 | ||
| 020 | |a 9781586038212 | ||
| 035 | |a (OCoLC)994402860 |z (OCoLC)1227951224 |z (OCoLC)1264943552 | ||
| 050 | 4 | |a QA279.5 .R54 2008 | |
| 082 | 0 | 4 | |a 519.5 |a 519.5/42 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Riggelsen, C. | |
| 245 | 1 | 0 | |a Approximation Methods for Efficient Learning of Bayesian Networks. |
| 246 | 3 | |a Frontiers in Artificial Intelligence and Applications | |
| 260 | |a Amsterdam : |b IOS Press, |c 2008. | ||
| 300 | |a 1 online resource (148 pages). | ||
| 336 | |a text |b txt | ||
| 337 | |a computer |b c | ||
| 338 | |a online resource |b cr | ||
| 490 | 1 | |a Frontiers in artificial intelligence and applications | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References. | |
| 520 | |a 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. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 7 | |a Bayesian statistical decision theory. |2 fast |0 (OCoLC)fst00829019 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 830 | 0 | |a Frontiers in artificial intelligence and applications. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=221156 |z Texto completo |
| 994 | |a 92 |b IZTAP | ||