http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Probabilistic reasoning in intelligent systems
Revised second printing, revised & updated edition.
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.
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.
Revised second printing, revised & updatedition
Morgan Kaufmann series in representation and reasoning