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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support - Softcover
Synopsis
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.
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About the Author
Phil Gregory is Professor Emeritus at the Department of Physics and Astronomy at the University of British Columbia.
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- PublisherCambridge University Press
- Publication date2010
- ISBN 10 0521150124
- ISBN 13 9780521150125
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages488
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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support
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ISBN 10: 0521150124
ISBN 13: 9780521150125
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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support
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ISBN 13: 9780521150125
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Paperback. Condition: new. Paperback. 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521150125
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