Bayesian Logical Data Analysis for the Physical Sciences A Comparative Approach with Mathematica® Support
Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support by Philip Christopher Gregory, published by Cambridge University Press on April 14, 2005, is an illustrated edition comprising 468 pages. This book presents a comprehensive overview of Bayesian inference, emphasizing its application in data analysis within the physical sciences. It explains how experimenters can assign probabilities to competing hypotheses based on existing knowledge and incorporates relevant prior information to enhance model parameter estimates.
Readers will find a clear exposition of key concepts, supported by numerous worked examples and problem sets. The text covers implementation techniques, including an introduction to Markov chain Monte-Carlo integration and model fitting. Extensive discussions on spectral analysis, including Fourier methods, are included, along with a dedicated chapter on Bayesian inference with Poisson sampling. Additionally, the book features three chapters on frequentist methods, bridging the gap between Bayesian and frequentist approaches. Supporting Mathematica® notebooks with solutions and additional examples are available online, enhancing the learning experience.
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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.
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