Data analysis: a Bayesian tutorial

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Oxford University Press, Jul 27, 2006 - Business & Economics - 246 pages
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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'.

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User Review  - PDExperiment626 - LibraryThing

This book not only is a good book to learn Bayesian statistics from, but it's also a great reference for the subject as well. Taking a very hands-on approach, the concepts and philosophy of Bayesian ... Read full review

Contents

Parameter estimation II
34
Model selection
78
Assigning probabilities
103
Copyright

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About the author (2006)


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