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  3. Bayesian Logical Data Analysis for the Physical Sciences
  • Cited by 542
Publisher:
Cambridge University Press
Online publication date:
September 2012
Print publication year:
2005
Online ISBN:
9780511791277
DOI:
https://doi.org/10.1017/CBO9780511791277
Subjects:
Physics and Astronomy, Astronomy: General Interest, Statistics for Physical Sciences and Engineering, Statistics and Probability
Information

Book description

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.

Reviews

'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.'

Source: Zentralblatt MATH

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

Source: 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 Source: Technometrics

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