Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis / Information Science and Statistics Bd.22 (PDF)
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. 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 or her level of understanding. The techniques and methods presented on model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined based on numerous courses the authors have held for practitioners worldwide.
Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen of HUGIN EXPERT A/S holds a PhD on probabilistic networks and is an Adjunct Professor of Computer Science at Aalborg University.
Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen of HUGIN EXPERT A/S holds a PhD on probabilistic networks and is an Adjunct Professor of Computer Science at Aalborg University.
- Autoren: Uffe B. Kjærulff , Anders L. Madsen
- 2012, 2nd ed. 2013, 382 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1461451043
- ISBN-13: 9781461451044
- Erscheinungsdatum: 30.11.2012
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- Grösse: 3.28 MB
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“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)
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