Studies in Inductive Logic and Probability, Volume 2A basic system of inductive logic; An axiomatic foundation for the logic of inductive generalization; A survey of inductive systems; On the condition of partial exchangeability; Representation theorems of the de finetti type; De finetti's generalizations of excahngeability; The structure of probabilities defined on first-order languages; A subjectivit's guide to objective chance. |
Contents
A Basic System of Inductive Logic Part 2 by Rudolf | 6 |
Basic and Admissible Regions in the Attribute | 20 |
The Analogy Influence | 32 |
The Eta Function | 50 |
Some Special Kinds of Families | 72 |
LambdaFamilies | 84 |
The Limit Axioms | 120 |
The Problem of a more | 145 |
A Survey of Inductive Systems by Theo A F Kuipers | 183 |
On the Condition of Partial Exchangeability by Bruno | 193 |
Representation Theorems of the de Finetti Type by Godehard | 207 |
De Finettis Generalizations of Exchangeability by Persi | 233 |
The Structure of Probabilities Defined on FirstOrder Lan | 251 |
A Subjectivists Guide to Objective Chance by David | 263 |
A Note on Regularity | 295 |
An Axiomatic Foundation for the Logic of Inductive Generali | 157 |
Common terms and phrases
6-function A-condition A-family A-system A₁ analogous assume assumption atomic proposition attribute space attribute symmetry axiom B₁ basic attributes basic regions Borel set Carnap's Cartesian product chance of heads coin concept condition consider convex set corresponding defined definition denumerable determined distance function distribution endpoint chance equal example family F Finetti Finetti's theorem finite following holds formulas frequency given H₁ hence Hintikka individuals inductive logic infinite initial credence function interval IS(A k-tuple kind language Lebesgue measure Let F mathematical induction measure function metric MI(s n-values n₁ normalized o-additive P₁ pairs parameter partially exchangeable partition plausible posterior probability Principal Principle probability distribution probability measure probability theory Proof r₁ real numbers reasonable initial credence representative function respect rule s₁ sample satisfies sequence subset supervenient Suppose tion tosses unique values width function X₁ y-equality y₁ zero