Uncertain inference /

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Bibliographic Details
Author / Creator:	Kyburg, Henry Ely, 1928-
Imprint:	Cambridge, UK ; New York : Cambridge University Press, 2001.
Description:	1 online resource (xii, 298 pages) : illustrations
Language:	English
Subject:	Uncertainty (Information theory) Probabilities. Logic, Symbolic and mathematical. Logic, Symbolic and mathematical. Probabilities. Uncertainty (Information theory) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11129708

Hidden Bibliographic Details
Other authors / contributors:	Teng, Choh Man.
ISBN:	0511020686 9780511020681 9780511612947 051161294X 9780521800648 0521800641 9780521001014 0521001013 9786610430291 6610430292 0511174799 9780511174797 0511154844 9780511154843 0511302398 9780511302398 0511047487 9780511047480
Digital file characteristics:	data file
Notes:	Includes bibliographical references and indexes. English. Print version record.
Summary:	"Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. This book seeks to provide a clear exposition of these approaches within a unified framework." "The principal market for the book will be students and professionals in philosophy, computer science, and artificial intelligence. Among the special features of the book are a chapter on evidential probability, an interpretation of probability specifically developed with an eye to inductive and uncertain inference, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement that concern matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of artificial intelligence and computer science."--Jacket.
Other form:	Print version: Kyburg, Henry Ely, 1928- Uncertain inference. Cambridge, UK ; New York : Cambridge University Press, 2001 0521800641 0521001013

Table of Contents:

Preface
1. Historical Background
1.1. Introduction
1.2. Inference
1.3. Roots in the Past
1.4. Francis Bacon
1.5. The Development of Probability
1.6. John Stuart Mill
1.7. G. H. von Wright
1.8. Bibliographical Notes
1.9. Exercises
Bibliography
2. First Order Logic
2.1. Introduction
2.2. Syntax
2.3. Semantics
2.4. W. V. O. Quine's Mathematical Logic
2.5. Arguments from Premises
2.6. Limitations
2.7. Summary
2.8. Bibliographical Notes
2.9. Exercises
Bibliography
3. The Probability Calculus
3.1. Introduction
3.2. Elementary Probability
3.2.1. Combinations and Permutations
3.2.2. The Probability Calculus
3.2.3. Elementary Theorems
3.3. Conditional Probability
3.3.1. The Axiom of Conditional Probability
3.3.2. Bayes' Theorem
3.4. Probability Distributions
3.4.1. Frequency Functions and Distribution Functions
3.4.2. Properties of Distributions
3.5. Sampling Distributions
3.6. Useful Distributions
3.7. Summary
3.8. Bibliographical Notes
3.9. Exercises
Bibliography
4. Interpretations of Probability
4.1. Introduction
4.2. The Classical View
4.3. Empirical Interpretations of Probability
4.3.1. The Limiting Frequency Interpretation
4.3.2. The Propensity Interpretation
4.4. Logical Interpretations of Probability
4.5. Subjective Interpretations of Probability
4.5.1. Dutch Book
4.5.2. Conditionalization
4.6. Summary
4.7. Bibliographical Notes
4.8. Exercises
Bibliography
5. Nonstandard Measures of Support
5.1. Support
5.2. Karl Popper
5.2.1. Corroboration
5.2.2. Levi's Criticism
5.3. Other Measures
5.4. Dempster-Shafer Belief Functions
5.4.1. Belief Functions and Mass Functions
5.4.2. Reduction to Sets of Probabilities
5.4.3. Combining Evidence
5.4.4. Special Cases
5.4.5. Assessment of Belief Functions
5.5. Sets of Probability Functions
5.6. Summary
5.7. Bibliographical Notes
5.8. Exercises
Bibliography
6. Nonmonotonic Reasoning
6.1. Introduction
6.2. Logic and (Non)monotonicity
6.3. Default Logic
6.3.1. Preliminaries
6.3.2. Transformation of Open Default Theories
6.3.3. Extensions
6.3.4. Need for a Fixed Point
6.3.5. Number of Extensions
6.3.6. Representation
6.3.7. Variants of Default Logic
6.4. Autoepistemic Logic
6.4.1. Modal Logic
6.4.2. Autoepistemic Reasoning vs Default Reasoning
6.4.3. Stable Expansions
6.4.4. Alternative Fixed-Point Formulation
6.4.5. Groundedness
6.5. Circumscription
6.6. Unresolved Issues
6.6.1. "Intuition": Basis of Defaults
6.6.2. Computational Complexity
6.6.3. Multiple Extensions
6.7. Summary
6.8. Bibliographical Notes
6.9. Exercises
Bibliography
7. Theory Replacement
7.1. Introduction
7.2. Theory Change
7.2.1. Expansion
7.2.2. Contraction
7.2.3. Revision
7.3. Rationality Considerations
7.4. The AGM Postulates
7.4.1. Expansion
7.4.2. Contraction
7.4.3. Revision
7.5. Connections
7.6. Selecting a Contraction Function
7.7. Epistemic Entrenchment
7.8. Must It Be?
7.8.1. Belief Bases
7.8.2. Updates
7.8.3. Rationality Revisited
7.8.4. Iterated Change
7.9. Summary
7.10. Bibliographical Notes
7.11. Exercises
Bibliography
8. Statistical Inference
8.1. Introduction
8.2. Classical Statistics
8.2.1. Significance Tests
8.2.2. Hypothesis Testing
8.2.3. Confidence Intervals
8.3. Bayesian Statistics
8.4. Summary
8.5. Bibliographical Notes
8.6. Exercises
Bibliography
9. Evidential Probability
9.1. Introduction
9.2. Background Issues and Assumptions
9.3. The Syntax of Statistical Knowledge
9.4. Reference Classes and Target Classes
9.4.1. Reference Formulas
9.4.2. Target Formulas
9.5. Prima Facie Support
9.5.1. Indefinite Probabilities
9.5.2. Definite Probabilities
9.6. Sharpening
9.6.1. Precision
9.6.2. Specificity
9.6.3. Richness
9.6.4. Sharpens
9.7. Partial Proof
9.8. Extended Example
9.9. A Useful Algorithm
9.10. Relations to Other Interpretations
9.11. Summary
9.12. Bibliographical Notes
9.13. Exercises
Bibliography
10. Semantics
10.1. Introduction
10.2. Models and Truth
10.3. Model Ratios
10.4. Relevant Models
10.5. Partial Validity
10.6. Remarks
10.7. Summary
10.8. Bibliographical Notes
10.9. Exercises
Bibliography
11. Applications
11.1. Introduction
11.2. Elementary Results
11.3. Inference from Samples
11.4. Example
11.5. Statistical Induction
11.6. Bayesian Induction
11.7. Sequences of Draws
11.8. Summary
11.9. Bibliographical Notes
11.10. Exercises
Bibliography
12. Scientific Inference
12.1. Introduction
12.1.1. Objectivity
12.1.2. Evidential and Practical Certainty
12.1.3. Statistical Inference
12.2. Demonstrative Induction
12.3. Direct Measurement
12.4. Indirect Measurement
12.5. Theory, Language, and Error
12.6. Summary
12.7. Bibliographical Notes
12.7.1. Measurement
12.7.2. Theories
12.7.3. Datamining
12.8. Exercises
Bibliography
Names Index
Index

Uncertain inference /

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