- Hardcover: 260 pages
- Publisher: OUP Oxford; 2 edition (1 Jun 2006)
- Language English
- ISBN-10: 0198568312
- ISBN-13: 978-0198568315
- Product Dimensions: 23.6 x 16.2 x 2.8 cm
- Average Customer Review: 4.8 out of 5 stars See all reviews (4 customer reviews)
- Amazon Bestsellers Rank: 1,180,428 in Books (See Top 100 in Books)
- See Complete Table of Contents
Data Analysis: A Bayesian Tutorial
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Product Description
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 )
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'.
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Most Helpful Customer Reviews
6 of 6 people found the following review helpful
Format: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.
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
Format: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.
Fantastic book that I have used countless times over the last year.
2 of 2 people found the following review helpful
Format:Paperback|Amazon Verified Purchase
I rarely write reviews on Amazon but I have to say here that of the many, many books on Bayesian theory and practice that I have read over 20 years of running a consultancy which specialises in the use of these techniques, this is certainly the best as an introduction to the modern approach to Bayesian thinking in scientific problems.
After the first chapter shows why the ideas are important and where they came from, it exudes practical advice rather then unnecessary theory and continues in a carefully-considered fashion developing the complexity and background until at the end we are exposed to some pretty advanced ideas where the appropriate level of theory is then injected.
Once you have absorbed the various messages thoroughly including e.g.
- the caveats
- how to specify realistic prior knowledge
- where approximations are useful and when they are not
you will be armed to use your own expert knowledge to attack problems which - although they may at first seem to be unmanageable - will be forced to yield to the subtlety and power of probability theory via Bayes' theorem if you can collect enough data of useful quality.
I disagree strongly with one of the other reviewers here who likes everything except the section on Nested Sampling by John Skilling at the end. It may be a little different in tone but the technique is sound, important and rather easy to implement, and variations have been making waves in difficult high-dimensional problems in areas such as astrophysics for years now. It has a bright future and this is an excellent introduction to it.
If you are interested in the modern Bayesian perspective and want real gravity, rigour and depth (along with long-winded bluster, humour and personal attacks on critics) then go for Jaynes' "Probability Theory: the Logic of Science"
Probability Theory: The Logic of Science: Principles and Elementary Applications Vol 1
which is the 'reference book' (though untypical in form & slightly unfinished) to support this excellent practical introduction.
After the first chapter shows why the ideas are important and where they came from, it exudes practical advice rather then unnecessary theory and continues in a carefully-considered fashion developing the complexity and background until at the end we are exposed to some pretty advanced ideas where the appropriate level of theory is then injected.
Once you have absorbed the various messages thoroughly including e.g.
- the caveats
- how to specify realistic prior knowledge
- where approximations are useful and when they are not
you will be armed to use your own expert knowledge to attack problems which - although they may at first seem to be unmanageable - will be forced to yield to the subtlety and power of probability theory via Bayes' theorem if you can collect enough data of useful quality.
I disagree strongly with one of the other reviewers here who likes everything except the section on Nested Sampling by John Skilling at the end. It may be a little different in tone but the technique is sound, important and rather easy to implement, and variations have been making waves in difficult high-dimensional problems in areas such as astrophysics for years now. It has a bright future and this is an excellent introduction to it.
If you are interested in the modern Bayesian perspective and want real gravity, rigour and depth (along with long-winded bluster, humour and personal attacks on critics) then go for Jaynes' "Probability Theory: the Logic of Science"
Probability Theory: The Logic of Science: Principles and Elementary Applications Vol 1
which is the 'reference book' (though untypical in form & slightly unfinished) to support this excellent practical introduction.
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