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Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences

A Comparative Approach with Mathematica (R) Support

by Phil Gregory
Paperback
Publication Date: 20/05/2010

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$107.95
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 (R) notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
ISBN:
9780521150125
9780521150125
Category:
Probability & statistics
Format:
Paperback
Publication Date:
20-05-2010
Language:
English
Publisher:
Cambridge University Press
Country of origin:
United Kingdom
Pages:
488
Dimensions (mm):
244x170x28mm
Weight:
0.77kg
Phil Gregory

Born in Southampton, England, Phil Gregory first came to Australia in 1977. He has contributed to a number of important ornithological works, including Handbook of the Birds of the World.

Special interests include field identification, vocalisation, taxonomy and distribution. He became a full time birding guide in 1998 and now based in Kuranda he leads several tours a year throughout the Pacific Region.

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