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Bayesian Logical Data Analysis for the Physical Sciences
A Comparative Approach with Mathematica® Support

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  • 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.

    • Introduces statistical inference in the larger context of scientific methods, and includes many worked examples and problem sets
    • Presents Bayesian theory but also compares and contrasts with other existing ideas
    • Mathematica® support notebook is available for readers from www.cambridge.org/9780521150125
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    Reviews & endorsements

    "All researchers and scientists who are interested in the Bayesian scientific paradigm can benefit greatly from the examples and illustrations here. It is a welcome addition to the vast literature on Bayesian inference."
    Sreenivasan Ravi, University of Mysore, Manasagangotri

    "The book can easily keep the readers amazed and attracted to its content throughout the read and make them want to return back to it recursively. It presents a perfect balance between theoretical inference and a practical know-how approach to Bayesian methods."
    Stan Lipovetsky, GfK Custom Research North America, Technometrics

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    Product details

    • Date Published: May 2005
    • format: Adobe eBook Reader
    • isbn: 9780511081385
    • contains: 132 b/w illus. 74 exercises
    • availability: Adobe Reader ebooks available from eBooks.com
  • Table of 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 (Gaussian errors)
    10. Linear model fitting (Gaussian errors)
    11. Nonlinear model fitting
    12. Markov Chain Monte Carlo
    13. Bayesian spectral analysis
    14. Bayesian inference (Poisson sampling)
    Appendix A. Singular value decomposition
    Appendix 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.

  • Resources for

    Bayesian Logical Data Analysis for the Physical Sciences

    Phil Gregory

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  • Author

    Phil Gregory, University of British Columbia, Vancouver
    Phil Gregory is Professor Emeritus at the Department of Physics and Astronomy at the University of British Columbia.

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