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LEADER: 03760fam a2200421 a 4500
001 1953560
005 20220609034517.0
008 960718t19971997nyua b 001 0 eng
010 $a 96033161
020 $a0387948589 (New York : acid-free paper)
035 $a(OCoLC)35138631
035 $a(OCoLC)ocm35138631
035 $9AMF3591CU
035 $a(NNC)1953560
035 $a1953560
040 $aDLC$cDLC$dDLC$dOrLoB-B
050 00 $aQA76.76.E95$bC378 1997
082 00 $a006.3/3$220
100 1 $aCastillo, Enrique.$0http://id.loc.gov/authorities/names/n78086337
245 10 $aExpert systems and probabilistic network models /$cEnrique Castillo, José Manuel Gutiérrez, Ali S. Hadi.
260 $aNew York :$bSpringer,$c[1997], ©1997.
300 $axiv, 605 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aMonographs in computer science
504 $aIncludes bibliographical references (p. [581]-596) and index.
505 00 $g1.$tIntroduction --$g2.$tRule-Based Expert Systems --$g3.$tProbabilistic Expert Systems --$g4.$tSome Concepts of Graphs --$g5.$tBuilding Probabilistic Models --$g6.$tGraphically Specified Models --$g7.$tExtending Graphically Specified Models --$g8.$tExact Propagation in Probabilistic Network Models --$g9.$tApproximate Propagation Methods --$g10.$tSymbolic Propagation of Evidence --$g11.$tLearning Bayesian Networks --$g12.$tCase Studies.
520 $aExpert systems and uncertainty in artificial intelligence have seen a great surge of research activity during the last decade. This book provides a clear and up-to-date account of the research progress in these areas.
520 8 $aThe authors begin with a survey of rule-based expert systems, which are mainly applicable to deterministic situations. Since most practical applications involve some degree of uncertainty, the authors then introduce probabilistic expert systems to deal with this element of uncertainty. They build on this foundation by showing how coherent expert systems are constructed and how probabilistic models such as Bayesian and Markov networks are developed.
520 8 $aSubsequent chapters discuss how knowledge is updated by using both exact and approximate propagation methods. Other subjects such as symbolic propagation, sensitivity analysis, and learning are also presented. The book concludes with a chapter that applies the methods presented in the book to some case studies of real-life applications.
520 8 $a. The concepts, ideas, and algorithms are illustrated by more than 150 examples and more than 250 graphs with the aid of computer programs developed by the authors. These programs can be obtained from a World Wide Web site (see the address in the preface). The book also includes end-of-chapter exercises and an extensive bibliography.
520 8 $aThis book is intended for advanced undergraduate and graduate students, and for research workers and professionals from a variety of fields, including computer science, applied mathematics, statistics, engineering, medicine, business, economics, and social sciences. No previous knowledge of expert systems is assumed. Readers are assumed to have some background in probability and statistics.
650 0 $aExpert systems (Computer science)$0http://id.loc.gov/authorities/subjects/sh85046450
650 0 $aProbabilities.$0http://id.loc.gov/authorities/subjects/sh85107090
700 1 $aGutiérrez, José Manuel.$0http://id.loc.gov/authorities/names/n2005052450
700 1 $aHadi, Ali S.$0http://id.loc.gov/authorities/names/n87891894
830 0 $aMonographs in computer science.$0http://id.loc.gov/authorities/names/n96033210
852 00 $boff,eng$hQA76.76.E95$iC378 1997