Probabilistic reasoning in intelligent systems : networks of plausible inference /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pearl, Judea (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Francisco, Calif. : Morgan Kaufmann Publishers, [1988]
Edición:Revised second printing, revised & updated edition.
Colección:Morgan Kaufmann series in representation and reasoning.
Temas:
Artificial intelligence.
Intelligence artificielle.
artificial intelligence.
COMPUTERS > General.
Artificial intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
  • Copyright Page
  • Table of Contents
  • Dedication
  • Preface
  • 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
  • 2.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  • Exercises
  • 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?
  • 4.6 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  • Exercises
  • 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
  • 6.3 THE VALUE OF INFORMATION
  • 6.4 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
  • 8.3 LEARNING HIDDEN CAUSE
  • 8.4 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.

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