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Probabilistic Reasoning in Intelligent Systems

Book Description

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 provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.


Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. THE MORGAN KAUFMANN SERIES IN REPRESENTATION AND REASONING
  5. Copyright
  6. Dedication
  7. Preface
  8. Preface to the Fourth Printing
  9. Chapter 1: UNCERTAINTY IN AI SYSTEMS: AN OVERVIEW
    1. Publisher Summary
    2. 1.1 INTRODUCTION
    3. 1.2 EXTENSIONAL SYSTEMS: MERITS, DEFICIENCIES, AND REMEDIES
    4. 1.3 INTENSIONAL SYSTEMS AND NETWORK REPRESENTATIONS
    5. 1.4 THE CASE FOR PROBABILITIES
    6. 1.5 QUALITATIVE REASONING WITH PROBABILITIES
  10. Chapter 2: BAYESIAN INFERENCE
    1. Publisher Summary
    2. 2.1 BASIC CONCEPTS
    3. 2.2 HIERARCHICAL MODELING
    4. 2.3 EPISTEMOLOGICAL ISSUES OF BELIEF UPDATING
    5. SUMMARY
    6. 2.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  11. Chapter 3: MARKOV AND BAYESIAN NETWORKS: Two Graphical Representations of Probabilistic Knowledge
    1. 3.1 FROM NUMERICAL TO GRAPHICAL REPRESENTATIONS
    2. SUMMARY
    3. 3.2 MARKOV NETWORKS
    4. SUMMARY
    5. 3.3 BAYESIAN NETWORKS
    6. 3.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
    7. Appendix 3-A Proof of Theorem 3
    8. Appendix 3-B Proof of Theorem 4
  12. Chapter 4: BELIEF UPDATING BY NETWORK PROPAGATION
    1. Publisher Summary
    2. 4.1 AUTONOMOUS PROPAGATION AS A COMPUTATIONAL PARADIGM
    3. 4.2 BELIEF PROPAGATION IN CAUSAL TREES
    4. 4.3 BELIEF PROPAGATION IN CAUSAL POLYTREES (SINGLY CONNECTED NETWORKS)
    5. SUMMARY
    6. 4.4 COPING WITH LOOPS
    7. CONCLUSIONS
    8. 4.5 WHAT ELSE CAN BAYESIAN NETWORKS COMPUTE?
  13. Chapter 5: DISTRIBUTED REVISION OF COMPOSITE BELIEFS
    1. Publisher Summary
    2. 5.1 INTRODUCTION
    3. 5.2 ILLUSTRATING THE PROPAGATION SCHEME
    4. 5.3 BELIEF REVISION IN SINGLY CONNECTED NETWORKS
    5. 5.4 DIAGNOSIS OF SYSTEMS WITH MULTIPLE FAULTS
    6. 5.5 APPLICATION TO MEDICAL DIAGNOSIS
    7. 5.6 THE NATURE OF EXPLANATIONS
    8. 5.7 CONCLUSIONS
    9. 5.8 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  14. Chapter 6: DECISION AND CONTROL
    1. Publisher Summary
    2. 6.1 FROM BELIEFS TO ACTIONS: INTRODUCTION TO DECISION ANALYSIS
    3. 6.2 DECISION TREES AND INFLUENCE DIAGRAMS
    4. 6.3 THE VALUE OF INFORMATION
    5. 6.4 RELEVANCE-BASED CONTROL
    6. 6.5 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  15. Chapter 7: TAXONOMIC HIERARCHIES, CONTINUOUS VARIABLES, AND UNCERTAIN PROBABILITIES
    1. Publisher Summary
    2. 7.1 EVIDENTIAL REASONING IN TAXONOMIC HIERARCHIES
    3. 7.2 MANAGING CONTINUOUS VARIABLES
    4. CONCLUSIONS
    5. 7.3 REPRESENTING UNCERTAINTY ABOUT PROBABILITIES
    6. 7.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
    7. Appendix 7-A Derivation of Propagation Rules For Continuous Variables
  16. Chapter 8: LEARNING STRUCTURE FROM DATA
    1. Publisher Summary
    2. 8.1 CAUSALITY, MODULARITY, AND TREE STRUCTURES
    3. 8.2 STRUCTURING THE OBSERVABLES
    4. 8.3 LEARNING HIDDEN CAUSES
    5. 8.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
    6. Appendix 8-A Proof of Theorems 1 and 2
    7. Appendix 8-B Conditions for Star-Decomposability (After Lazarfeld [1966])
  17. Chapter 9: NON-BAYESIAN FORMALISMS FOR MANAGING UNCERTAINTY
    1. Publisher Summary
    2. 9.1 THE DEMPSTER-SHAFER THEORY
    3. 9.2 TRUTH MAINTENANCE SYSTEMS
    4. 9.3 PROBABILISTIC LOGIC
    5. SUMMARY
    6. 9.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  18. Chapter 10: LOGIC AND PROBABILITY: THE STRANGE CONNECTION
    1. Publisher Summary
    2. 10.1 INTRODUCTION TO NONMONOTONIC REASONING
    3. 10.2 PROBABILISTIC SEMANTICS FOR DEFAULT REASONING
    4. CONCLUSIONS
    5. 10.3 EMBRACING CAUSALITY IN DEFAULT REASONING
    6. SUMMARY
    7. 10.4 A PROBABILISTIC TREATMENT OF THE YALE SHOOTING PROBLEM
    8. 10.5 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
  19. Bibliography
  20. Author Index
  21. Subject Index