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Springer Bayesian Inference: Parameter Estimation and Decisions (Advanced Texts in Physics)

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Description

Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. The text is written at introductory level, with many examples and exercises. Review From the reviews:"The book under review combines features of a textbook and a monograph. … Arguments are presented as explicitly as possible with the aid of appendices … . There are numerous examples and illustrations, often taken from physics research. Problems are posed and their solutions are provided." (Joseph Melamed, Zentralblatt MATH, Vol. 1019, 2003) From the Back Cover The book provides a generalization of Gaussian error intervals tosituations where the data follow non-Gaussian distributions. Thisusually occurs in frontier science, where the observed parameter isjust above background or the histogram of multiparametric datacontains empty bins. Then the validity of a theorycannot be decided by the chi-squared-criterion, but this long-standingproblem is solved here. The book is based on Bayes' theorem, symmetry anddifferential geometry. In addition to solutions of practical problems, the textprovides an epistemic insight: The logic of quantum mechanics isobtained as the logic of unbiased inference from counting data.However, no knowledge of quantum mechanics is required. The text,examples and exercises are written at an introductory level.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
15 December 2010
Listed Since
20 September 2010

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