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Data Analysis: A Bayesian Tutorial
Contributor(s): Sivia, Devinderjit (Author), Skilling, John (Author)

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 ISBN: 0198568320     ISBN-13: 9780198568322
Publisher: Oxford University Press, USA
OUR PRICE: $53.20  

Binding Type: Paperback - See All Available Formats & Editions
Published: July 2006
Qty:

Annotation: 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'.
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - Bayesian Analysis
Dewey: 519.542
Physical Information: 0.57" H x 7.56" W x 9.14" L (0.91 lbs) 264 pages
Features: Bibliography, Illustrated, Index, Table of Contents
Review Citations: Scitech Book News 12/01/2006 pg. 33
 
Descriptions, Reviews, Etc.
Publisher Description:
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'.
 
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