Synopses & Reviews
These sparkling essays by a gifted thinker offer philosophical views on the roots of statistical interference. A pioneer in the early development of computing, Irving J. Good made fundamental contributions to the theory of Bayesian inference and was a key member of the team that broke the German Enigma code during World War II. Good maintains that a grasp of probability is essential to answering both practical and philosophical questions. This compilation of his most accessible works concentrates on philosophical rather than mathematical subjects, ranging from rational decisions, randomness, and the nature of probability to operational research, artificial intelligence, cognitive psychology, and chess.
These twenty-three self-contained articles represent the author's work in a variety of fields but are unified by a consistently rational approach. Five closely related sections explore Bayesian rationality; probability; corroboration, hypothesis testing, and simplicity; information and surprise; and causality and explanation. A comprehensive index, abundant references, and a bibliography refer readers to classic and modern literature. Good's thought-provoking observations and memorable examples provide scientists, mathematicians, and historians of science with a coherent view of probability and its applications.
This in-depth treatment of probability theory by a famous British statistician explores Keynesian principles and surveys such topics as Bayesian rationality, corroboration, hypothesis testing, and mathematical tools for induction and simplicity. 1983 edition.
Table of Contents
AcknowledgmentsIntroductionPart I. Bayesian Rationality1. Rational Decisions2. Twenty-seven Principles of Rationality3. 46656 Varieties of Bayesians4. The Bayesian Influence, or How to Sweep Subjectivism under the CarpetPart II. Probability5. Which Comes First, Probability or Statistics6. Kinds of Probability7. Sublective Probability as the Measure of a Non-measurable Set8. Random Thoughts about Randomness9. Some History of the Hierarchical Bayesian Methodology10. Dynamic Probability, Computer Chess, and the Measurement of KnowledgePart III . Corroboration, Hypothesis Testing, Induction, and Simplicity11. The White Shoe is a Red Herring12. The White Shoe qua Herring is Pink13. A Sublective Evaluation of Bode's Law and an "Objective" Test for Approximate Numerical Rationality14.Some Logic and History of Hypothesis Testing15. Explicativity, Corroboration, and the Relative Odds of HypothesisPart IV Information and Surprise16. The Appropriate Mathematical Tools for Describing and Measuring Uncertainty17. On the Principle of Total Evidence18.A Little Learning Can Be Dangerous19. The Probabilistic Explication of Information, Evidence, Surprise, Causality, Explanation, and Utility20. Is the Size of Our Galaxy Surprising?Part V. Causality and Explanation21. A Causal Calculus22. A Simplification in the "Causal Calculus"23. Explicativity: A Mathematical Theory of Explanations with Statistical ApplicationsReferencesBibliographySublect Index of the BibliographyName IndexSublect Index