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'.
Product details
- Paperback | 264 pages
- 159 x 233 x 15mm | 408g
- 27 Jul 2006
- Oxford University Press
- Oxford, United Kingdom
- English
- Revised
- 2nd Revised edition
- 68 line drawings + 1 halftone
- 0198568320
- 9780198568322
- 123,090
Download Data Analysis : A Bayesian Tutorial (9780198568322).pdf, available at globalexpertsystems.org for free.
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