Data Analysis: A Bayesian Tutorial
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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 )
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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 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'.
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Most Helpful Customer Reviews
6 of 6 people found the following review helpful:
4.0 out of 5 stars
Excellent with a bad part at the end,
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This review is from: Data Analysis: A Bayesian Tutorial (Paperback)
I had looked at a couple of other books on Bayesian statistics and noticed that they all focus mainly on all kind of very technical aspects. Sivia's book on the other hand deals almost exclusively with basic applications of Bayes theorem. It does this by discussing a number of examples in detail. This makes it much more useful people like me (I am a physical chemist), as I can often simply copy what Sivia does. I rarely feel the need to consult the more advanced books.
In the second edition of the book a chapter on nested sampling has been added written by John Skilling. I have seen that this technique can be very useful, but only because I found a paper in which it was described much better than in the book. It is not just that a completely different notation is used by Skilling, also the whole approach is different. Whereas Sivia aims to write an introductory textbook, Skilling seems to write for specialists. This really lessens the quality of the book.
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Best introduction to data analysis from a Bayesian point of view,
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This review is from: Data Analysis: A Bayesian Tutorial (Paperback)
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.
2 of 2 people found the following review helpful:
5.0 out of 5 stars
A fantastic introduction to Bayesian analyses,
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This review is from: Data Analysis: A Bayesian Tutorial (Paperback)
This is a _great_ book. The early chapters which introduce the broad concepts underlying Bayesian reasoning are particularly strong. Although it's aimed at students of physics, it would be useful to a much broader range of disciplines (I'm a psychiatrist which is about as far from physics as you can get...).
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