Bayesian Logical Data Analysis for the Physical Sciences

A Comparative Approach with Mathematica® Support

Phil Gregory author

Format:Paperback

Publisher:Cambridge University Press

Published:20th May '10

Currently unavailable, and unfortunately no date known when it will be back

This paperback is available in another edition too:

Bayesian Logical Data Analysis for the Physical Sciences cover

A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.

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.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.

'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

ISBN: 9780521150125

Dimensions: 244mm x 170mm x 28mm

Weight: 770g

488 pages