Optimum inductive methods : a study in inductive probability, Bayesian statistics, and verisimilitude
著者
書誌事項
Optimum inductive methods : a study in inductive probability, Bayesian statistics, and verisimilitude
（Synthese library, v. 232）
Kluwer Academic Publishers, c1993
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注記
Bibliography: p. 177-184
Includes indexes
内容説明・目次
内容説明
This book deals with a basic problem arising within the Bayesian approach 1 to scientific methodology, namely the choice of prior probabilities. The problem will be considered with special reference to some inference methods used within Bayesian statistics (BS) and the so-called theory of inductive 2 probabilities (T/P). In this study an important role will be played by the assumption - defended by Sir Karl Popper and the supporters of the current verisimilitude theory (VT) - that the cognitive goal of science is the achievement of a high degree of truthlikeness or verisimilitude. A more detailed outline of the issues and objectives of the book is given in Section 1. In Section 2 the historical background of the Bayesian approach and the verisimilitude theory is briefly illustrated. In Section 3, the methods used in TIP and BS for making multinomial inference~ are considered and some conceptual relationships between TIP and BS are pointed out. In Section 4 the main lines of a new approach to the problem of the choice of prior probabilities are illustrated. Lastly, in Section 5 >the structure of the book is described and a first explanation of some technical terms is provided.
目次
1. Introduction. Part I: Inductive Probabilities, Bayesian Statistics, and Verisimilitude. 2. The Theory of Inductive Probabilities: Basic Features and Applications. 3. Bayesian Statistics and Mulinomial Inferences: Basic Features. 4. Bayesian Point Estimation, Verisimilitude, and Immodesty. Part II: De Finetti's Theorem, GC-Systems, and Dirichlet Distributions. 5. Exchangeable Inductive Methods, Bayesian Statistics, and Convergence towards the Truth. 6. GC-Systems and Dirichlet Distributions. Part III: Verisimilitude, Disorder, and Optimum Prior Probabilities. 7. The Choice of Priors Probabilities: the Subjective, Aprioristic, and Contextual Approaches. 8. The Epistemic problem of Optimaility (EPO): a Contextual Approach. 9. The Contextual Approach to EPO: Comparisons with Other Views. 10. Disordered Universes: Diversity Measures in Statistics and the Empirical Sciences. 11. Concluding Remarks. Notes. References. Index of Names. Index of Subjects. List of Requirements and Acronyms.
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