Data Analysis A Bayesian Tutorial [Paperback]

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'.

1. The Basics,Sivia
2. Parameter Estimation I,Sivia
3. Parameter Estimation II,Sivia
4. Model Selection,Sivia
5. Assigning Probabilities,Sivia
6. Non-parametric Estimation,Sivia
7. Experimental Design,Sivia
8. Least-Squares Extensions,Sivia
9. Nested Sampling,Skilling
10. Quantification,Skilling
Appendices
Bibliography

Review from previous editionProviding 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 greatl³Ô