Probabilistic Reasoning in Intelligent Systems

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