Data Analysis (2^nd Ed.) A Bayesian Tutorial

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

Senior undergraduate and graduate students in physics, chemistry, and engineering. Researchers across a broad spectrum of experimental scientific disciplines. Not for statisticians.

Devinderjit Singh Sivia Rutherford Appleton Laboratory Chilton Oxon OX11 5DJ John Skilling Maximum Entropy Data Consultants 42 Southgate Street Bury St Edmonds Suffolk IP33 2AZ

One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples.