Skip to content
Register Sign in Wishlist

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

  • Author: Phil Gregory, University of British Columbia, Vancouver
  • Date Published: May 2010
  • availability: Available
  • format: Paperback
  • isbn: 9780521150125


Add to wishlist

Other available formats:
Hardback, eBook

Looking for an inspection copy?

This title is not currently available for inspection. However, if you are interested in the title for your course we can consider offering an inspection copy. To register your interest please contact providing details of the course you are teaching.

Product filter button
About the Authors
  • 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

    • 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
    Read more

    Reviews & endorsements

    '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 sampling.' Zentralblatt MATH

    'The examples are well integrated with the text and are enlightening.' Contemporary Physics

    '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, Technometrics

    See more reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity


    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?


    Product details

    • Date Published: May 2010
    • format: Paperback
    • isbn: 9780521150125
    • length: 488 pages
    • dimensions: 244 x 170 x 28 mm
    • weight: 0.77kg
    • contains: 132 b/w illus. 74 exercises
    • availability: Available
  • Table of Contents

    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

  • Resources for

    Bayesian Logical Data Analysis for the Physical Sciences

    Phil Gregory

    General Resources

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.

    Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

    Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

    If you are having problems accessing these resources please contact

  • 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.

Related Books

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner Please see the permission section of the catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×
warning icon

Turn stock notifications on?

You must be signed in to your Cambridge account to turn product stock notifications on or off.

Sign in Create a Cambridge account arrow icon

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.


Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

Please fill in the required fields in your feedback submission.