Networks of Plausible Inference To order this title, and for more information, click here
By Judea Pearl
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.
Contents
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl Revised Second Printing
Chapter 1 Uncertainty In AI Systems: An Overview
1.1 Introduction
1.1.1 Why bother with Uncertainty?
1.1.2 Why Is It a Problem?
1.1.3 Approaches to Uncertainty
1.1.4 Extensional vs. Intensional Approaches
1.2 Extensional Systems: Merits, Deficiencies, and Remedies
1.2.1 Computational Merits
1.2.2 Semantic Deficiencies
1.2.3 Attempted Remedies and their Limitations
1.3 Intensional Systems and Network Representations
1.3.1
Why Networks?
1.3.2 Graphoids and the Formalization of Relevance and Causality
1.4 The Case for Probabilities
1.4.1 Why Should Beliefs Combine Like Frequencies?
1.4.2 The Primitive Relationships of Probability Language
1.4.3
Probability as a Faithful Guardian of Common Sense
1.5 Qualitative Reasoning With Probabilities
1.5.1 Softened
Logic vs. Hardened Probabilities
1.5.2 Probabilities and the Logic of "Almost True"
1.6 Bibliographical and
Historical Remarks
Chapter 2 Bayesian Inference
2.1 Basic Concepts
2.1.1 Probabilistic
Formulation and Bayesian Inversion
2.1.2 Combining Predictive and Diagnostic Supports
2.1.3 Pooling of Evidence
2.1.4 Recursive Bayesian Updating
2.1.5 Multi-Valued Hypotheses
2.2 Hierarchical Modeling
2.2.1
Uncertain Evidence (Cascaded Inference)
2.2.2 Virtual (Intangible) Evidence
2.2.3 Predicting Future Events
2.2.4
Multiple Causes and "Explaining Away"
2.2.5 Belief Networks and the Role of Causality
2.3 Epistemological
Issues of Belief Updating
2.3.1 Patterns of Plausible Inference: Polya vs. Bayes?
2.3.2 The Three Prisoners Paradox:
When the Bare Facts Won't Do
2.3.3 Jeffrey's Rule and the Problem of Autonomous Inference Agents
2.4 Bibliographical
and Historical Remarks
Exercises
Chapter 3 Markov and Bayesian Networks: Two Graphical Representations
of Probabilistic Knowledge
3.1 From Numeral to Graphical Representations
3.1.1 Introduction
3.1.2 An
Axiomatic Basis for Probabilistic Dependencies
3.1.3 On Representing Dependencies by Undirected Graphs
3.1.4 Axiomatic
Characterization of Graph-Isomorph Dependencies
3.2 Markov Networks
3.2.1 Definitions and Formal Properties
3.2.2 Illustrations
3.2.3 Markov Network as a Knowledge Base
3.2.4 Decomposable Models
3.3 Bayesian
Networks
3.3.1 Dependence Semantics for Bayesian Networks
3.3.2 Bayesian Network as a Knowledge Base
3.3.3
How Expressive are 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
4.1.1 Constraint Propagation and Rule-based Computation
4.1.2
Why Probabilistic Reasoning Seems to Resist Propagation
4.2 Belief Propagation in Causal Trees
4.2.1 Notation
4.2.2 Propagation in Chains
4.2.3 Propagation in Trees
4.3 Belief Propagation in Causal Polytrees (Singly
Connected Networks)
4.3.1 Propagation Rules
4.3.2 Canonical Models of Multicausal Interactions
4.4
Coping with Loops
4.4.1 Clustering Methods
4.4.2 The Method of Conditioning (Reasoning by Assumptions)
4.4.3
Stochastic Simulation
4.5 What Else Can Bayesian Networks Compute?
4.5.1 Answering Queries
4.5.2
Introducing Constraints
4.5.3 Answering Conjunctive Queries
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
5.2.1 Belief Updating (A Review)
5.2.2 Belief Revision
5.3
Belief Revision in Singly Connected Networks
5.3.1 Deriving the Propagation Rules
5.3.2 Summary of Propagation Rules
5.3.3 Reaching Equilibrium and Assembling a Composite Solution
5.3.4 Comparison to Belief Updating
5.3.5 Coping
with Loops
5.4 Diagnosis of Systems with Multiple Faults
5.4.1 An Example: Electronic Circuit
5.4.2
Initialization
5.4.3 Fault Interpretation
5.4.4 Finding the Second-Best Interpretation
5.5 Application
to Medical Diagnosis
5.5.1 The Causal Model
5.5.2 Message Propagation
5.5.3 Choosing the Best Interpretation
5.5.4 Generating Explanations
5.5.5 Reversibility vs. Perseverance
5.6 The Nature of Explanations
5.6.1
Accepting vs. Assessing Beliefs
5.6.2 Is a Most-Probable Explanation Adequate?
5.6.3 Circumscribing 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
6.1.1. Rational Decisions and Quality
Guarantees
6.1.2 Consequences, Payoffs, and Lotteries
6.1.3 Calibrating the Value of a Lottery
6.1.4 The Axioms
of Utility Theory
6.1.5 Utility Functions
6.2 Decision Trees and Influence Diagrams
6.2.1 Decision
Trees
6.2.2 Planning with Decision Trees
6.2.3 Influence Diagrams
6.3 The Value of Information
6.3.1
Information Sources and Their Values
6.3.2 Myopic Assessments of Information Sources
6.4 Relevance-Based
Control
6.4.1 Focusing Attention
6.4.2 Utility-Free Assessment of Information Sources
6.4.3 Controlling Attention
6.4.4 Summary
6.5 Bibliographical and Historical Remarks
Exercises
Chapter
7 Taxonomic Hierarchies, Continuous Variables, and Uncertain Probabilities
7.1 Evidential Reasoning in Taxonomic Hierarchies
7.1.1 Taxonomic vs. Causal Hierarchies
7.1.2 Evidence Propagation in Taxonomic Hierarchies
7.1.3 Probabilistic
Justification
7.1.4 Psychological and Computational Characteristics
7.2 Managing Continuous Variables
7.2.1
Plausible Reasoning about Uncertain Quantities
7.2.2 Propagating Estimates and Ranges
7.2.3 Qualitative Patterns of
Reasoning
7.2.4 Discussion
7.3 Representing Uncertainty About Probabilities
7.3.1 The Semantics of
Probabilities of Probabilities
7.3.2 De Finetti's Paradigm of Uncertain Contingencies
7.3.3 A Formal Definition of
Network-Induced Confidence Measures
7.3.4 The Effect of Evidence on Confidence: An Example
7.3.5 Conclusions
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 Structurs
8.2 Structuring the Observables
8.2.1 Chow's Method of Constructing Trees
8.2.2 Structuring Causal Polytrees
8.2.3 Conclusions: Causation or Covariation?
8.3 Learning Hidden Causes
8.3.1 Problem Definition and Nomenclature
8.3.2 Star-Decomposable Triplets
8.3.3 A Tree-Reconstruction Procedure
8.3.4 Extensions to Normal Variables
8.3.5 Conclusions and Open Questions
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
9.1.1 Basic Concepts
9.1.2 Comparing Bayesian and Dempster-Shafer Formalisms
9.1.3 Dempster's Rule of Combination
9.1.4 More on the
Nature of Probability Intervals
9.1.5 Applications to Rule-bases Systems
9.1.6 Bayes vs. Demster-Shafer: A Semantic
Clash
9.2 Truth Maintenance Systems
9.2.1 Naming the Assumptions
9.2.2 Uncertainty Management in
an ATMS
9.2.3 Incidence Calculus
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
10.1.1 Reiter's Default Logic
10.1.2 Problems with Default Logics
10.1.3 Empirical
vs. Procedural Semantics in Default Reasoning
10.2 Probabilistic Semantics for Default Reasoning
10.2.1 (-semantics
10.2.2 Axiomatic Characterization and a System of Defeasible Inference
10.2.3 Relevance-based Conventions
10.2.4
Do People Use the Logic of Extreme Probabilities?
10.3 Embracing Causality in Default Reasoning
10.3.1 How
Old Beliefs Were Established Determines Which New Beliefs are Evoked
10.3.2 More on the Distrinction Between Causal and Evidential
Support
10.3.3 The C-E System: A Coarse Logical Abstraction of Causal Directionality
10.3.4 Implicit Suppressors and
the Need for Finer Abstractions
10.4 A Probabilistic Treatment of the Yale Shooting Problem
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