
- 800 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Inductive Logic
About this book
Inductive Logic is number ten in the 11-volume Handbook of the History of Logic. While there are many examples were a science split from philosophy and became autonomous (such as physics with Newton and biology with Darwin), and while there are, perhaps, topics that are of exclusively philosophical interest, inductive logic — as this handbook attests — is a research field where philosophers and scientists fruitfully and constructively interact. This handbook covers the rich history of scientific turning points in Inductive Logic, including probability theory and decision theory. Written by leading researchers in the field, both this volume and the Handbook as a whole are definitive reference tools for senior undergraduates, graduate students and researchers in the history of logic, the history of philosophy, and any discipline, such as mathematics, computer science, cognitive psychology, and artificial intelligence, for whom the historical background of his or her work is a salient consideration.- Chapter on the Port Royal contributions to probability theory and decision theory- Serves as a singular contribution to the intellectual history of the 20th century- Contains the latest scholarly discoveries and interpretative insights
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Table of contents
- Cover
- Contents
- Copyright
- Introduction
- Contributors
- Induction before Hume
- Hume and the Problem of Induction
- The Debate between Whewell and Mill on the Nature of Scientific Induction
- An Explorer upon Untrodden Ground
- The Modern Epistemic Interpretations of Probability
- Popper and Hypothetico-deductivism
- Hempel and the Paradoxes of Confirmation
- Carnap and the Logic of Inductive Inference
- The Development of the Hintikka Program
- Hans Reichenbach’s Probability Logic
- Goodman and the Demise of Syntactic and Semantic Models
- The Development of Subjective Bayesianism
- Varieties of Bayesianism
- Inductive Logic and Empirical Psychology
- Inductive Logic and Statistics
- Statistical Learning Theory: Models, Concepts, and Results
- Formal Learning Theory in Context
- Mechanizing Induction
- Index