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Approximation methods for efficient learning of Bayesian networks [electronic resource] / Carsten Riggelsen.
- 作者： Riggelsen, Carsten.
- 其他題名：
- Frontiers in artificial intelligence and applications..
- Frontiers in artificial intelligence and applications ;
- 出版： Amsterdam ;Washington, DC : IOS Press c2008.
- 叢書名： Dissertations in artificial intelligence , Frontiers in artificial intelligence and applications. ;v. 168
- 主題： Bayesian statistical decision theory. , Machine learning. , Neural networks (Computer science) , Electronic books
- ISBN： 9781586038212 、 9781607502982
- URL：
click for full text (IOS Press)
- 一般註：Electronic reproduction
- 書目註：Includes bibliographical references (p. [133]-137).
-
讀者標籤：
- 系統號： 005166198 | 機讀編目格式
館藏資訊
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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