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dc.contributor.authorKjærulff, Uffe B.-
dc.contributor.authorMadsen, Anders L.-
dc.date.accessioned2017-04-11T08:13:08Z-
dc.date.available2017-04-11T08:13:08Z-
dc.date.issued2008-
dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/26268-
dc.description.abstractProbabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.-
dc.format.extent325 p.-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectMathematicsen_US
dc.subjectStatistics ; Uncertainty (Information theory) ; Bayesian statistical decision theory ; Expert systems (Computer science)en_US
dc.subject.ddc003.54-
dc.titleBayesian Networks and Influence Diagramsen_US
dc.typeBooken_US
Appears in Collections:Khoa học máy tính & Công nghệ thông tin


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  • Full metadata record
    DC FieldValueLanguage
    dc.contributor.authorKjærulff, Uffe B.-
    dc.contributor.authorMadsen, Anders L.-
    dc.date.accessioned2017-04-11T08:13:08Z-
    dc.date.available2017-04-11T08:13:08Z-
    dc.date.issued2008-
    dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/26268-
    dc.description.abstractProbabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.-
    dc.format.extent325 p.-
    dc.language.isoenen_US
    dc.publisherSpringeren_US
    dc.subjectMathematicsen_US
    dc.subjectStatistics ; Uncertainty (Information theory) ; Bayesian statistical decision theory ; Expert systems (Computer science)en_US
    dc.subject.ddc003.54-
    dc.titleBayesian Networks and Influence Diagramsen_US
    dc.typeBooken_US
    Appears in Collections:Khoa học máy tính & Công nghệ thông tin


  • 347.pdf
    • Size : 3,29 MB

    • Format : Adobe PDF

    • View : 
    • Download :