Optimum Inductive Methods: A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude: 232 (Synthese Library, 232) - Image 1

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Springer Optimum Inductive Methods: A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude: 232 (Synthese Library, 232)

Name: Optimum Inductive Methods: A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude: 232 (Synthese Library, 232)
Brand: Springer
Price: 72.62 GBP
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Philosophy of Science

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Description

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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 9048143187
Category: Books > Subjects > Science, Nature & Maths > History & Philosophy > Philosophy of Science
Domain: Amazon UK
Release Date: 07 December 2010
Listed Since: 20 September 2010

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Springer Optimum Inductive Methods: A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude: 232 (Synthese Library, 232)

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