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...
| Otros Autores: | , , , , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
San Francisco, California :
Morgan Kaufmann Publishers, Inc
1988.
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| Edición: | Revised second printing |
| Colección: | Morgan Kaufmann series in representation and reasoning.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629653406719 |
Tabla de Contenidos:
- 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
- 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 Auxiliary 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 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
- 10.3 EMBRACING CAUSALITY IN DEFAULT REASONING
