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Probabilistic reasoning in intelligent systems : networks of plausible inference /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco, CA :
Morgan Kaufmann Publishers,
[1988]
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| Edición: | Revised second printing. |
| Colección: | Morgan Kaufmann series in representation and reasoning.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | OR_ocn927108286 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 151028s1988 caua ob 001 0 eng d | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d OCLCF |d CEF |d OCLCQ |d UAB |d RDF |d DST |d OCLCO |d OCLCQ | ||
| 020 | |a 9780080514895 | ||
| 020 | |a 0080514898 | ||
| 020 | |z 9781558604797 | ||
| 029 | 1 | |a GBVCP |b 897158938 | |
| 035 | |a (OCoLC)927108286 | ||
| 037 | |a CL0500000664 |b Safari Books Online | ||
| 050 | 4 | |a TA347.A78 | |
| 082 | 0 | 4 | |a 006.3 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Pearl, Judea, |e author. | |
| 245 | 1 | 0 | |a Probabilistic reasoning in intelligent systems : |b networks of plausible inference / |c Judea Pearl. |
| 250 | |a Revised second printing. | ||
| 264 | 1 | |a San Francisco, CA : |b Morgan Kaufmann Publishers, |c [1988] | |
| 264 | 4 | |c ©1988 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a The Morgan Kaufmann series in representation and reasoning | |
| 588 | 0 | |a Online resource; title from title page (Safari, viewed October 26, 2015). | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 505 | 0 | |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 | |
| 505 | 8 | |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? | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 520 | |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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Reasoning. | |
| 650 | 0 | |a Probabilities. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Probability | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Probabilités. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a probability. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Probabilities. |2 fast |0 (OCoLC)fst01077737 | |
| 650 | 7 | |a Reasoning. |2 fast |0 (OCoLC)fst01091282 | |
| 650 | 7 | |a Probabilitats. |2 lemac | |
| 830 | 0 | |a Morgan Kaufmann series in representation and reasoning. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780080514895/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||