LEADER 05536nam 2200697Ia 450 001 9910139802403321 005 20200520144314.0 010 $a1-282-27888-6 010 $a9786612278884 010 $a0-470-74955-5 010 $a0-470-74956-3 035 $a(CKB)1000000000790823 035 $a(EBL)470176 035 $a(OCoLC)648759664 035 $a(SSID)ssj0000354259 035 $a(PQKBManifestationID)11256487 035 $a(PQKBTitleCode)TC0000354259 035 $a(PQKBWorkID)10313201 035 $a(PQKB)10307526 035 $a(MiAaPQ)EBC470176 035 $a(Au-PeEL)EBL470176 035 $a(CaPaEBR)ebr10333027 035 $a(CaONFJC)MIL227888 035 $a(EXLCZ)991000000000790823 100 $a20090728d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGraphical models $emethods for data analysis and mining /$fChristian Borgelt, Matthias Steinbrecher & Rudolf Kruse 205 $a2nd ed. 210 $aHoboken, NJ $cJohn Wiley$dc2009 215 $a1 online resource (405 p.) 225 1 $aWiley series in computational statistics 300 $aDescription based upon print version of record. 311 $a0-470-72210-X 320 $aIncludes bibliographical references and index. 327 $aGraphical Models; Contents; Preface; 1 Introduction; 1.1 Data and Knowledge; 1.2 Knowledge Discovery and Data Mining; 1.2.1 The KDD Process; 1.2.2 Data Mining Tasks; 1.2.3 Data Mining Methods; 1.3 Graphical Models; 1.4 Outline of this Book; 2 Imprecision and Uncertainty; 2.1 Modeling Inferences; 2.2 Imprecision and Relational Algebra; 2.3 Uncertainty and Probability Theory; 2.4 Possibility Theory and the Context Model; 2.4.1 Experiments with Dice; 2.4.2 The Context Model; 2.4.3 The Insufficient Reason Principle; 2.4.4 Overlapping Contexts; 2.4.5 Mathematical Formalization 327 $a2.4.6 Normalization and Consistency2.4.7 Possibility Measures; 2.4.8 Mass Assignment Theory; 2.4.9 Degrees of Possibility for Decision Making; 2.4.10 Conditional Degrees of Possibility; 2.4.11 Imprecision and Uncertainty; 2.4.12 Open Problems; 3 Decomposition; 3.1 Decomposition and Reasoning; 3.2 Relational Decomposition; 3.2.1 A Simple Example; 3.2.2 Reasoning in the Simple Example; 3.2.3 Decomposability of Relations; 3.2.4 Tuple-Based Formalization; 3.2.5 Possibility-Based Formalization; 3.2.6 Conditional Possibility and Independence; 3.3 Probabilistic Decomposition; 3.3.1 A Simple Example 327 $a3.3.2 Reasoning in the Simple Example3.3.3 Factorization of Probability Distributions; 3.3.4 Conditional Probability and Independence; 3.4 Possibilistic Decomposition; 3.4.1 Transfer from Relational Decomposition; 3.4.2 A Simple Example; 3.4.3 Reasoning in the Simple Example; 3.4.4 Conditional Degrees of Possibility and Independence; 3.5 Possibility versus Probability; 4 Graphical Representation; 4.1 Conditional Independence Graphs; 4.1.1 Axioms of Conditional Independence; 4.1.2 Graph Terminology; 4.1.3 Separation in Graphs; 4.1.4 Dependence and Independence Maps 327 $a4.1.5 Markov Properties of Graphs4.1.6 Markov Equivalence of Graphs; 4.1.7 Graphs and Decompositions; 4.1.8 Markov Networks and Bayesian Networks; 4.2 Evidence Propagation in Graphs; 4.2.1 Propagation in Undirected Trees; 4.2.2 Join Tree Propagation; 4.2.3 Other Evidence Propagation Methods; 5 Computing Projections; 5.1 Databases of Sample Cases; 5.2 Relational and Sum Projections; 5.3 Expectation Maximization; 5.4 Maximum Projections; 5.4.1 A Simple Example; 5.4.2 Computation via the Support; 5.4.3 Computation via the Closure; 5.4.4 Experimental Evaluation; 5.4.5 Limitations 327 $a6 Naive Classifiers6.1 Naive Bayes Classifiers; 6.1.1 The Basic Formula; 6.1.2 Relation to Bayesian Networks; 6.1.3 A Simple Example; 6.2 A Naive Possibilistic Classifier; 6.3 Classifier Simplification; 6.4 Experimental Evaluation; 7 Learning Global Structure; 7.1 Principles of Learning Global Structure; 7.1.1 Learning Relational Networks; 7.1.2 Learning Probabilistic Networks; 7.1.3 Learning Possibilistic Networks; 7.1.4 Components of a Learning Algorithm; 7.2 Evaluation Measures; 7.2.1 General Considerations; 7.2.2 Notation and Presuppositions; 7.2.3 Relational Evaluation Measures 327 $a7.2.4 Probabilistic Evaluation Measures 330 $aGraphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of dis 410 0$aWiley series in computational statistics. 606 $aData mining 606 $aMathematical statistics$xGraphic methods 615 0$aData mining. 615 0$aMathematical statistics$xGraphic methods. 676 $a006.3/12 700 $aBorgelt$b Christian$0280169 701 $aSteinbrecher$b Matthias$0522159 701 $aKruse$b Rudolf$0102093 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139802403321 996 $aGraphical Models$9835672 997 $aUNINA