Data Analysis

A Bayesian Tutorial

By: Devinderjit Sivia, John Skilling

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Paperback | 1 June 2006 | Edition Number 2

At a Glance

Paperback
264 Pages

Edition Type
Revised

Dimensions(cm)
23.2 x 15.7 x 1.6

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Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.

This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.

The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.

Industry Reviews

`Review from previous edition Providing a clear rationale for some of the most widely used procedures.' European Journal of Engineering Education `This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practical advice for the beginner, at an elementary level that will be grasped readily by every science or engineering student. ' Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003

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