Bayesian Logical Data Analysis for the Physical Sciences
A Comparative Approach with Mathematica® Support
(Sprache: Englisch)
A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.
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A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.
Klappentext zu „Bayesian Logical Data Analysis for the Physical Sciences “
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.
Inhaltsverzeichnis zu „Bayesian Logical Data Analysis for the Physical Sciences “
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.
Autoren-Porträt von Phil Gregory
Gregory, PhilPhil Gregory is Professor Emeritus at the Department of Physics and Astronomy at the University of British Columbia.
Bibliographische Angaben
- Autor: Phil Gregory
- 488 Seiten, 132 Abbildungen, Maße: 17 x 24,4 cm, Kartoniert (TB), Englisch
- Verlag: Cambridge University Press
- ISBN-10: 0521150124
- ISBN-13: 9780521150125
- Erscheinungsdatum: 19.05.2011
Sprache:
Englisch
Rezension zu „Bayesian Logical Data Analysis for the Physical Sciences “
'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
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