| Description |
1 online resource (148 pages). |
|
text file |
| Series |
Frontiers in artificial intelligence and applications Approximation methods for efficient learning of Bayesian networks
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| Contents |
Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References. |
| Summary |
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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Language |
English. |
| Subject |
Bayesian statistical decision theory.
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Bayesian statistical decision theory. |
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Machine learning.
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Machine learning. |
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Neural networks (Computer science)
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Neural networks (Computer science) |
| Genre/Form |
Electronic books.
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| Added Title |
Frontiers in Artificial Intelligence and Applications |
| ISBN |
1586038214 |
|
9781586038212 |
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