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Bayesian Inference (Hardcover)

by Hanns L. Harney (Author), H.L. Harney (Author) "Science does not prove anything..." (more)
Key Phrases: marginalisation paradox, incoherent alternative, commuting parameters (more...)
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Editorial Reviews
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)



Product Description

Filling a longstanding need in the physical sciences, Bayesian Inference offers the first basic introduction for advanced undergraduates and graduates in the physical sciences. This text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This usually occurs in frontier science because the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins. In this case, the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. Requiring no knowledge of quantum mechanics, the text is written on introductory level, with many examples and exercises, for physicists planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.

 



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Inside This Book (learn more)
First Sentence:
Science does not prove anything. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
marginalisation paradox, incoherent alternative, commuting parameters, parameters commute, uninteresting parameters, silent parameter, form invariance, cubic die, minor posterior, kth bin, multinomial model, expectation value with respect, minor model, normalising factor, conditional measure, form invariant, multinomial theorem, geometric measure, multiplication function, first derivative vanishes, error interval, minor measures, invariant measure, complete fit, multinomial distribution
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover | Surprise Me!
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Citations (learn more)
This book cites 42 books:
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