An edition of Probabilistic reasoning in intelligent systems
(1988)
Probabilistic Reasoning in Intelligent Systems
Networks of Plausible Inference
Revised 2nd printing.
by Judea Pearl
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Last edited by Tauriel063
January 18, 2025 | History
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Subjects
Artificial intelligence, Probabilities, Reasoning, Nonfiction| Edition | Availability |
|---|---|
1
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
2014, Elsevier Science & Technology Books
in English
0080514898 9780080514895
|
zzzz
|
2
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
1988, Morgan Kaufmann
in English
- Revised 2nd printing.
1558604790 9781558604797
|
aaaa
|
3
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
1988, Morgan Kaufmann Publishers
in English
0934613737 9780934613736
|
cccc
|
Book Details
Table of Contents
Chapter 1. Uncertainty in AI Systems: An Overview
1.1. Introduction
1.2. Extensional Systems: Merits, Deficiencies, and Remedies
1.3. Intentional Systems and Network Representations
1.4. The Case for Probabilities
1.5. Qualitative Reasoning with Probabilities
1.6. Bibliographic and Historical Remarks
Chapter 2. Bayesian Inference
2.1. Basic Concepts
2.2. Hierarchical Modeling
2.3. Epistemological issues of Belief Updating
2.4. Bibliographic and Historical Remarks
Chapter 3. Markov and Bayesian Networks: Two Graphical Representations of Probabilistic Knowledge
3.1. From Numerical to Graphical Representations
3.2. Markov Networks
3.3. Bayesian Networks
3.4. Bibliographic and Historical Remarks
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. Bibliographic and Historical Remarks
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. Bibliographic and Historical Remarks
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. Bibliographic and Historical Remarks
Chapter 7. Taxonomic Hierarchies, Continuous Variables, and Uncertain Probabilities
7.1. Evidential Reasoning in Taxonomic Hierarchies
7.2. Managing Continuous Variables
7.3. Representation Uncertainty about Probabilities
7.4. Bibliographic and Historical Remarks
Chapter 8. Learning Structure from Data
8.1. Causality, Modularity, and Tree Structures
8.2. Structuring the Observables
8.3. Learning Hidden Causes
8.4. Bibliographic and Historical Remarks
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. Bibliographic and Historical Remarks
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
10.4. A Probabilistic Treatment of the Yale Shooting Problem
10.5. Bibliographic and Historical Remarks
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| January 18, 2025 | Edited by Tauriel063 | Bulk tagging works |
| February 14, 2020 | Edited by MARC Bot | remove fake subjects |
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