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|a 9780080514895
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| 050 |
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|a TA347.A78
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| 100 |
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|a Pearl, Judea
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| 245 |
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|a Probabilistic reasoning in intelligent systems
|b networks of plausible inference
|c Judea Pearl
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| 250 |
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|a Revised second printing
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| 260 |
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|a San Francisco, CA
|b Morgan Kaufmann Publishers
|c 1988
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| 300 |
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|a 1 volume
|b illustrations
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| 505 |
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|a 2.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKSExercises; Chapter 3. MARKOV AND BAYESIAN NETWORKS; 3.1 FROM NUMERICAL TO GRAPHICAL REPRESENTATIONS; 3.2 MARKOV NETWORKS; 3.3 BAYESIAN NETWORKS; 3.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Exercises; APPENDIX 3-A Proof of Theorem 3; APPENDIX 3-B Proof of Theorem 4; Chapter 4. BELIEF UPDATING BY NETWORK PROPAGATION; 4.1 AUTONOMOUS PROPAGATION AS A COMPUTATIONAL PARADIGM; 4.2 BELIEF PROPAGATION IN CAUSAL TREES; 4.3 BELIEF PROPAGATION IN CAUSAL POLYTREES (SINGLY CONNECTED NETWORKS); 4.4 COPING WITH LOOPS; 4.5 WHAT ELSE CAN BAYESIAN NETWORKS COMPUTE?
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| 505 |
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|a 6.3 THE VALUE OF INFORMATION6.4 RELEVANCE-BASED CONTROL; 6.5 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Exercises; Chapter 7. TAXONOMIC HIERARCHIES, CONTINUOUS VARIABLES, AND UNCERTAIN PROBABILITIES; 7.1 EVIDENTIAL REASONING IN TAXONOMIC HIERARCHIES; 7.2 MANAGING CONTINUOUS VARIABLES; 7.3 REPRESENTING UNCERTAINTY ABOUT PROBABILITIES; 7.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Exercises; APPENDIX 7-A Derivation of Propagation Rules For Continuous Variables; Chapter 8. LEARNING STRUCTURE FROM DATA; 8.1 CAUSALITY, MODULARITY, AND TREE STRUCTURES; 8.2 STRUCTURING THE OBSERVABLES
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|a Front Cover; Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference; Copyright Page; Dedication; Preface; Table of Contents; Chapter 1. UNCERTAINTY IN AI SYSTEMS: AN OVERVIEW; 1.1 INTRODUCTION; 1.2 EXTENSIONAL SYSTEMS: MERITS, DEFICIENCIES, AND REMEDIES; 1.3 INTENSIONAL SYSTEMS AND NETWORK REPRESENTATIONS; 1.4 THE CASE FOR PROBABILITIES; 1.5 QUALITATIVE REASONING WITH PROBABILITIES; 1.6 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Chapter 2. BAYESIAN INFERENCE; 2.1 BASIC CONCEPTS; 2.2 HIERARCHICAL MODELING; 2.3 EPISTEMOLOGICAL ISSUES OF BELIEF UPDATING
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|a 4.6 BIBLIOGRAPHICAL AND HISTORICAL REMARKSExercises; APPENDIX 4-A Auxilliary Derivations for Section 4.5.3; Chapter 5. DISTRIBUTED REVISION OF COMPOSITE BELIEFS; 5.1 INTRODUCTION; 5.2 ILLUSTRATING THE PROPAGATION SCHEME; 5.3 BELIEF REVISION IN SINGLY CONNECTED NETWORKS; 5.4 DIAGNOSIS OF SYSTEMS WITH MULTIPLE FAULTS; 5.5 APPLICATION TO MEDICAL DIAGNOSIS; 5.6 THE NATURE OF EXPLANATIONS; 5.7 CONCLUSIONS; 5.8 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Exercises; Chapter 6. DECISION AND CONTROL; 6.1 FROM BELIEFS TO ACTIONS: INTRODUCTION TO DECISION ANALYSIS; 6.2 DECISION TREES AND INFLUENCE DIAGRAMS
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| 505 |
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|a 8.3 LEARNING HIDDEN CAUSE8.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; EXERCISES; APPENDIX 8-A Proof of Theorems 1 and 2; APPENDIX 8-B Conditions for Star-Decomposability (After Lazarfeld [1966]); Chapter 9. NON-BAYESIAN FORMALISMS FOR MANAGING UNCERTAINTY; 9.1 THE DEMPSTER-SHAFER THEORY; 9.2 TRUTH MAINTENANCE SYSTEMS; 9.3 PROBABILISTIC LOGIC; 9.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS; Exercises; Chapter 10. LOGIC AND PROBABILITY: THE STRANGE CONNECTION; 10.1 INTRODUCTION TO NONMONOTONIC REASONING; 10.2 PROBABILISTIC SEMANTICS FOR DEFAULT REASONING
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| 505 |
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|a Includes bibliographical references and indexes
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| 653 |
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|a Probabilitats / lemac
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| 653 |
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|a Probabilities / fast
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| 653 |
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|a Reasoning / http://id.loc.gov/authorities/subjects/sh85111790
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| 653 |
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|a Artificial intelligence / fast
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| 653 |
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|a Probability
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a Probabilités
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a artificial intelligence / aat
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| 653 |
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|a Reasoning / fast
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| 653 |
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|a Probabilities / http://id.loc.gov/authorities/subjects/sh85107090
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| 653 |
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|a probability / aat
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a The Morgan Kaufmann series in representation and reasoning
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| 776 |
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|z 9781558604797
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9780080514895/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 519.2
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|a 006.3
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| 520 |
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|a Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provid
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