Data Analysis: A Bayesian Tutorial

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OUP Oxford, Jun 1, 2006 - Mathematics - 264 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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This is the best intro statistics book out there, period. A close second is Gregory's Bayesian text. If you are an experimentalist who needs statistics to get things done (i.e. who would rather skip the proofs and understand *practical* statistical data analysis), then this is the book for you. This is the primary textbook for three "Astro Stats" courses that i know of: mine at Caltech (Ay117), at UCLA and at UC Riverside. 

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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

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