000 03785nam a22005655i 4500
001 978-1-4614-5104-4
003 DE-He213
005 20140220082818.0
007 cr nn 008mamaa
008 121205s2013 xxu| s |||| 0|eng d
020 _a9781461451044
_9978-1-4614-5104-4
024 7 _a10.1007/978-1-4614-5104-4
_2doi
050 4 _aQA276-280
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aKjærulff, Uffe B.
_eauthor.
245 1 0 _aBayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
_h[electronic resource] /
_cby Uffe B. Kjærulff, Anders L. Madsen.
250 _aSecond Edition.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXVII, 382 p. 186 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Science and Statistics,
_x1613-9011 ;
_v22
505 0 _aIntroduction -- 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.
520 _aBayesian 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.
650 0 _aStatistics.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aOperations Research, Management Science.
650 2 4 _aProbability Theory and Stochastic Processes.
700 1 _aMadsen, Anders L.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461451037
830 0 _aInformation Science and Statistics,
_x1613-9011 ;
_v22
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-5104-4
912 _aZDB-2-SCS
999 _c95314
_d95314