{"product_id":"graphical-models-isbn-9780470722107","title":"Graphical Models","description":"Graphical 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 \u003ci\u003eGraphical Models\u003c\/i\u003e 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 distributions, and applications relating to graphical models and problems for further research.  \u003cb\u003ePreface\u003c\/b\u003e.  \u003cp\u003e\u003cb\u003e1 Introduction\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e1.1 Data and Knowledge.\u003c\/p\u003e \u003cp\u003e1.2 Knowledge Discovery and Data Mining.\u003c\/p\u003e \u003cp\u003e1.3 Graphical Models.\u003c\/p\u003e \u003cp\u003e1.4 Outline of this Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Imprecision and Uncertainty\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e2.1 Modeling Inferences.\u003c\/p\u003e \u003cp\u003e2.2 Imprecision and Relational Algebra.\u003c\/p\u003e \u003cp\u003e2.3 Uncertainty and Probability Theory.\u003c\/p\u003e \u003cp\u003e2.4 Possibility Theory and the Context Model.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Decomposition\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e3.1 Decomposition and Reasoning.\u003c\/p\u003e \u003cp\u003e3.2 Relational Decomposition.\u003c\/p\u003e \u003cp\u003e3.3 Probabilistic Decomposition.\u003c\/p\u003e \u003cp\u003e3.4 Possibilistic Decomposition.\u003c\/p\u003e \u003cp\u003e3.5 Possibility versus Probability.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Graphical Representation\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e4.1 Conditional Independence Graphs.\u003c\/p\u003e \u003cp\u003e4.2 Evidence Propagation in Graphs.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Computing Projections\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e5.1 Databases of Sample Cases.\u003c\/p\u003e \u003cp\u003e5.2 Relational and Sum Projections.\u003c\/p\u003e \u003cp\u003e5.3 Expectation Maximization.\u003c\/p\u003e \u003cp\u003e5.4 Maximum Projections.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Naive Classifiers\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e6.1 Naive Bayes Classifiers.\u003c\/p\u003e \u003cp\u003e6.2 A Naive Possibilistic Classifier.\u003c\/p\u003e \u003cp\u003e6.3 Classifier Simplification.\u003c\/p\u003e \u003cp\u003e6.4 Experimental Evaluation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Learning Global Structure\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e7.1 Principles of Learning Global Structure.\u003c\/p\u003e \u003cp\u003e7.2 Evaluation Measures.\u003c\/p\u003e \u003cp\u003e7.3 Search Methods.\u003c\/p\u003e \u003cp\u003e7.4 Experimental Evaluation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Learning Local Structure\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e8.1 Local Network Structure.\u003c\/p\u003e \u003cp\u003e8.2 Learning Local Structure.\u003c\/p\u003e \u003cp\u003e8.3 Experimental Evaluation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Inductive Causation\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e9.1 Correlation and Causation.\u003c\/p\u003e \u003cp\u003e9.2 Causal and Probabilistic Structure.\u003c\/p\u003e \u003cp\u003e9.3 Faithfulness and Latent Variables.\u003c\/p\u003e \u003cp\u003e9.4 The Inductive Causation Algorithm.\u003c\/p\u003e \u003cp\u003e9.5 Critique of the Underlying Assumptions.\u003c\/p\u003e \u003cp\u003e9.6 Evaluation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Visualization\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e10.1 Potentials.\u003c\/p\u003e \u003cp\u003e10.2 Association Rules.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Applications\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e11.1 Diagnosis of Electrical Circuits.\u003c\/p\u003e \u003cp\u003e11.2 Application in Telecommunications.\u003c\/p\u003e \u003cp\u003e11.3 Application at Volkswagen.\u003c\/p\u003e \u003cp\u003e11.4 Application at DaimlerChrysler.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Proofs of Theorems\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eA.1 Proof of Theorem 4.1.2.\u003c\/p\u003e \u003cp\u003eA.2 Proof of Theorem 4.1.18.\u003c\/p\u003e \u003cp\u003eA.3 Proof of Theorem 4.1.20.\u003c\/p\u003e \u003cp\u003eA.4 Proof of Theorem 4.1.26.\u003c\/p\u003e \u003cp\u003eA.5 Proof of Theorem 4.1.28.\u003c\/p\u003e \u003cp\u003eA.6 Proof of Theorem 4.1.30.\u003c\/p\u003e \u003cp\u003eA.7 Proof of Theorem 4.1.31.\u003c\/p\u003e \u003cp\u003eA.8 Proof of Theorem 5.4.8.\u003c\/p\u003e \u003cp\u003eA.9 Proof of Lemma .2.2.\u003c\/p\u003e \u003cp\u003eA.10 Proof of Lemma .2.4.\u003c\/p\u003e \u003cp\u003eA.11 Proof of Lemma .2.6.\u003c\/p\u003e \u003cp\u003eA.12 Proof of Theorem 7.3.1.\u003c\/p\u003e \u003cp\u003eA.13 Proof of Theorem 7.3.2.\u003c\/p\u003e \u003cp\u003eA.14 Proof of Theorem 7.3.3.\u003c\/p\u003e \u003cp\u003eA.15 Proof of Theorem 7.3.5.\u003c\/p\u003e \u003cp\u003eA.16 Proof of Theorem 7.3.7.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Software Tools\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \u003cp\u003e“The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.”  (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, 1 August 2013)\u003c\/p\u003e \"All of the necessary background is provided, with material on modeling under uncertainty and imprecision modeling, decomposition of distributions, graphical representation of distributions, applications relating to graphical models, and problems for further research.\" (\u003ci\u003eBook News\u003c\/i\u003e, December 2009)\u003cbr\u003e  \u003cp\u003e\u003cstrong\u003eChristian Borgelt\u003c\/strong\u003e, is the Principal researcher at the European Centre for Soft Computing at Otto-von-Guericke University of Magdeburg. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRudolf Kruse\u003c\/strong\u003e, Professor for Computer Science at Otto-von-Guericke University of Magdeburg. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eMatthias Steinbrecher\u003c\/strong\u003e, Department of Knowledge Processing and Language Engineering, School of Computer Science, Universitätsplatz 2,?Magdeburg, Germany.   The use of graphical models in applied statistics has increased considerably in recent years. At the same time the field of data mining has developed as a response to the large amounts of available data. This book addresses the overlap between these two important areas, highlighting the advantages of using graphical models for data analysis and mining. The Authors focus not only on probabilistic models such as Bayesian and Markov networks but also explore relational and possibilistic graphical models in order to analyse data sets.  \u003c\/p\u003e\u003cul\u003e \u003cli\u003ePresents all necessary background material including uncertainty and imprecision modeling, distribution decomposition and graphical representation.\u003c\/li\u003e \u003cli\u003eCovers Markov, Bayesian, relational and possibilistic networks.\u003c\/li\u003e \u003cli\u003eIncludes a new chapter on visualization and coverage of clique tree propagation, visualization techniques.\u003c\/li\u003e \u003cli\u003eDemonstrates learning algorithms based on a large number of different search methods and evaluation measures.\u003c\/li\u003e \u003cli\u003eIncludes a comprehensive bibliography and a detailed index.\u003c\/li\u003e \u003cli\u003eFeatures an accompanying website hosting exercises, teaching material and open source software.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearchers and practitioners who use graphical models in their work, graduate students of applied statistics, computer science and engineering will find much of interest in this new edition.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989308031205,"sku":"NP9780470722107","price":153.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470722107.jpg?v=1761783609","url":"https:\/\/k12savings.com\/es\/products\/graphical-models-isbn-9780470722107","provider":"K12savings","version":"1.0","type":"link"}