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Bayesian logical data analysis for the physical sciences : a comparative approach with Mathematica support

Author: P C Gregory
Publisher: Cambridge, UK ; New York : Cambridge University Press, 2005.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica notebooks are available.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Gregory, P.C. (Philip Christopher), 1941-
Bayesian logical data analysis for the physical sciences.
Cambridge, UK ; New York : Cambridge University Press, 2005
(DLC) 2004045930
(OCoLC)54953061
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: P C Gregory
ISBN: 052184150X 9780521841504 0511082282 9780511082283 0511081839 9780511081835 9780511791277 0511791275
OCLC Number: 60715238
Description: 1 online resource (xvii, 468 pages) : illustrations
Contents: Cover; Half-title; Title; Copyright; Contents; Preface; Acknowledgements; 1 Role of probability theory in science; 2 Probability theory as extended logic; 3 The how-to of Bayesian inference; 4 Assigning probabilities; 5 Frequentist statistical inference; 6 What is a statistic?; 7 Frequentist hypothesis testing; 8 Maximum entropy probabilities; 9 Bayesian inference with Gaussian errors; 10 Linear model fitting (Gaussian errors); 11 Nonlinear model fitting; 12 Markov chain Monte Carlo; 13 Bayesian revolution in spectral analysis; 14 Bayesian inference with Poisson sampling.
Responsibility: P.C. Gregory.
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Abstract:

A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.  Read more...

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'As well as the usual topics to be found in a text on Bayesian inference, chapters are included on frequentist inference (for contrast), non-linear model fitting, spectral analysis and Poisson Read more...

 
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