Product 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 optimisation, 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

• Amazon Sales Rank: #93793 in Books
• Published on: 2006-06-01
• Original language: English
• Number of items: 1
• Binding: Paperback
• 264 pages

Review
One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. (Katie St. Clair MAA Reviews )

About the Author
Devinderjit Singh Sivia
Rutherford Appleton Laboratory
Chilton
Oxon
OX11 5DJ

Customer Reviews

Best introduction to data analysis from a Bayesian point of view
I bought this book with the aim of improving my data analysis skills and also to try to figure out what it meant to do things the Bayesian way. In both cases this book did an admirable job. Due to the understandable explanations from first principles that this book offers it is possible to get a really intuitive feel to what is going (perhaps this is due to the Bayesian approach, as a physicist I felt that statistics was no longer just a complicated mix of formulas). In terms of getting a better grasp of data analysis I found that after reading and on occasion re-reading relevant chapters I have been able to apply it to actual problems in the field. The least squares extension chapter is particularly good in that it first highlights how some of the normal assumptions aren't appropriate before discussing how to proceed in those cases.

Fantastic book that I have used countless times over the last year.

excellent book
The second edition of this excellent textbook has extra chapters by Skilling (as in Gull and Skilling of maximum entropy fame) which begin to explore numerical issues. There is a discussion of Skilling's nested sampling technique, and sample code written (mercifully!) in C.

On the other hand, there is no discussion of the basics of MCMC or the Gibbs sampler, which I think detracts from the book somewhat. Presumably a desire to keep the book relatively short has something to do with this.