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  • 1
    Online Resource
    Online Resource
    Cambridge, U.K. ; : Cambridge University Press,
    UID:
    almafu_9959238036802883
    Format: 1 online resource (xvii, 468 pages) : , digital, PDF file(s).
    ISBN: 1-107-14026-9 , 1-107-38600-4 , 1-280-41586-X , 9786610415861 , 0-511-79127-5 , 0-511-17144-7 , 0-511-19717-9 , 0-511-08228-2 , 0-511-29855-2 , 0-511-08183-9
    Content: Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , 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 , Appendix A Singular value decompositionAppendix B Discrete Fourier Transforms; Appendix C Difference in two samples; Appendix D Poisson ON/OFF details; Appendix E Multivariate Gaussian from maximum entropy; References; Index , English
    Additional Edition: ISBN 0-521-15012-4
    Additional Edition: ISBN 0-521-84150-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_883426706
    Format: 1 Online-Ressource (xvii, 468 pages) , digital, PDF file(s)
    ISBN: 9780511791277
    Content: Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015)
    Additional Edition: ISBN 9780521841504
    Additional Edition: ISBN 9780521150125
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780521841504
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    gbv_1613937253
    Format: XVI, 468 S. , Ill., graph. Darst.
    Edition: 1. publ.
    ISBN: 0521150124 , 052184150X , 9780521150125 , 9780521841504
    Note: Includes bibliographical references and index
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Bayes-Verfahren ; Mathematica
    Library Location Call Number Volume/Issue/Year Availability
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