Data Analysis: A Bayesian Tutorial, 2/e (Paperback)

Devinderjit Sivia, John Skilling




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.



Table of Contents

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