03054nam 2200685Ia 450 991078259240332120230124182730.066117333371-281-73333-497866117333391-60750-298-4600-00-0346-31-4337-1131-1(CKB)1000000000554071(EBL)334196(OCoLC)437202842(SSID)ssj0000289022(PQKBManifestationID)11221425(PQKBTitleCode)TC0000289022(PQKBWorkID)10386010(PQKB)10753325(MiAaPQ)EBC334196(Au-PeEL)EBL334196(CaPaEBR)ebr10216841(CaONFJC)MIL173333(EXLCZ)99100000000055407120071215d2008 uy 0engur|n|---|||||txtccrApproximation methods for efficient learning of Bayesian networks[electronic resource] /Carsten RiggelsenAmsterdam ;Washington, DC IOS Pressc20081 online resource (148 p.)Frontiers in artificial intelligence and applications ;v. 168Dissertations in artificial intelligenceDescription based upon print version of record.1-58603-821-4 Includes bibliographical references (p. [133]-137).Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; ReferencesThis 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 tFrontiers in artificial intelligence and applications.Dissertations in artificial intelligence.Frontiers in artificial intelligence and applications ;v. 168.Bayesian statistical decision theoryMachine learningNeural networks (Computer science)Bayesian statistical decision theory.Machine learning.Neural networks (Computer science)519.5519.5/42Riggelsen Carsten1560683MiAaPQMiAaPQMiAaPQBOOK9910782592403321Approximation methods for efficient learning of Bayesian networks3826835UNINA