TY  - BOOK
AU  - Kjærulff,Uffe B.
AU  - Madsen,Anders L.
ED  - SpringerLink (Online service)
TI  - Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
T2  - Information Science and Statistics,
SN  - 9781461451044
AV  - QA276-280 
U1  - 519.5 23
PY  - 2013///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Statistics
KW  - Mathematical statistics
KW  - Data mining
KW  - Artificial intelligence
KW  - Operations research
KW  - Management science
KW  - Probabilities
KW  - Statistics and Computing/Statistics Programs
KW  - Probability and Statistics in Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence (incl. Robotics)
KW  - Operations Research, Management Science
KW  - Probability Theory and Stochastic Processes
N1  - Introduction -- Networks -- Probabilities -- Probabilistic Networks -- Solving Probabilistic Networks -- Eliciting the Model -- Modeling Techniques -- Data-Driven Modeling -- Conflict Analysis -- Sensitivity Analysis -- Value of Information Analysis -- Quick Reference to Model Construction -- List of Examples -- List of Figures -- List of Tables -- List of Symbols -- References -- Index
N2  - 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
UR  - http://dx.doi.org/10.1007/978-1-4614-5104-4
ER  -