Data Analysis: A Bayesian Tutorial by Devinderjit SiviaData Analysis: A Bayesian Tutorial by Devinderjit Sivia

Data Analysis: A Bayesian Tutorial

byDevinderjit Sivia, John Skilling

Paperback | June 1, 2006

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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'.
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
Title:Data Analysis: A Bayesian TutorialFormat:PaperbackDimensions:264 pages, 9.21 × 6.14 × 0.59 inPublished:June 1, 2006Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198568320

ISBN - 13:9780198568322


Table of Contents

1. Sivia: The Basics2. Sivia: Parameter Estimation I3. Sivia: Parameter Estimation II4. Sivia: Model Selection5. Sivia: Assigning Probabilities6. Sivia: Non-parametric Estimation7. Sivia: Experimental Design8. Sivia: Least-Squares Extensions9. Skilling: Nested Sampling10. Skilling: QuantificationAppendicesBibliography

Editorial Reviews

`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 great deal of practicaladvice for the beginner, at an elementary level that will be grasped readily by every science or engineering student.'Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003