Data Analysis: A Bayesian Tutorial [Paperback]

Devinderjit Sivia (Author), John Skilling (Author)

Book Description

Publication Date: July 27, 2006 | ISBN-10: 0198568320 | ISBN-13: 978-0198568322 | Edition: 2

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'.

Editorial Reviews

Review

"Review from previous edition Providing a clear rationale for some of the most widely used procedures."--European Journal of Engineering Education

"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 practical advice 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

About the Author

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

Product Details

• Paperback: 264 pages
• Publisher: Oxford University Press, USA; 2 edition (July 27, 2006)
• Language: English
• ISBN-10: 0198568320
• ISBN-13: 978-0198568322
• Product Dimensions: 6.3 x 0.6 x 9.2 inches
• Shipping Weight: 14.9 ounces (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #61,085 in Books (See Top 100 in Books)
  • #1 in Books > Professional & Technical > Professional Science > Physics > Entropy

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Most Helpful Customer Reviews

38 of 40 people found the following review helpful

4.0 out of 5 stars good July 7, 2008

By L. Antoine

Format:Paperback

This book is not really a tutorial for beginners as it goes directly into the subject. It is well written, rigorous, and not that expensive for people needing to learn the bayesian principles. For total beginners as I was, I would advise reading "Introduction to Bayesian Statistics" by Bolstad before this one. A good book on the topic, with good ideas and recent developments !

31 of 33 people found the following review helpful

5.0 out of 5 stars In-Depth and Practical August 3, 2007

By M. Schneider

Format:Paperback|Amazon Verified Purchase

Sivia and Skilling give a concise and clear exposition of Bayesian statistical analysis, and pair it with practical, real examples. It has been a great aid to me in doing actual data work. This text gets the balance of theoretical detail and practicality just right. In particular, abandoning the usual emphasis on analytical solutions and instead pairing real examples with numerical solution algorithms when appropriate, is perfect for someone concerned with applying Bayesian statistical analysis to real problems. A great and genuinely useful book!

30 of 32 people found the following review helpful

5.0 out of 5 stars Very good introduction May 18, 2008

By FG

Format:Paperback

This book is a must for those that are introducing themselves in bayesian statistics. It goes very strightforward in to the main topics and the mathematical notation is easy to follow. If you are just beginning I would recommend to read this book before Jaynes' book Probability Theory: The Logic of Science and after William M. Bolstad's Introduction to Bayesian Statistics

1 of 2 people found the following review helpful

5.0 out of 5 stars First rate introduction to Bayesian data analysis April 10, 2013

By William Evenson

Format:Paperback|Amazon Verified Purchase

Sivia is an innovative, long-time user of Bayesian data analysis. He is also an excellent teacher. This book puts the subject in context, connects to standard statistics, shows many examples, and illustrates the power of Bayesian methods when standard methods may not work well. Because he contextualizes the topic, this seems to me to be a excellent source for learning Bayesian data analysis as well as a good reference book.

1 of 2 people found the following review helpful

5.0 out of 5 stars An excellent book October 30, 2012

By Marcelo Chiapparini

Format:Paperback|Amazon Verified Purchase

This is an excellent book about the use of Bayesian statistic in data analysis. It taught me a lot, and even inspired me to apply the techniques presented in the book to my own research work. I highly recommend this book to anyone willing to learn about Bayesian statistic and applications. The book is very well written, with a lot of working examples.

1 of 2 people found the following review helpful

4.0 out of 5 stars Extremely useful October 22, 2012

By Pdecordoba

Format:Paperback|Amazon Verified Purchase

I've given the book four stars only because I don't feel qualified to give it five. Its exposition is truly masterful, partly because Sivia and Skilling are careful to explain the differences between quantities that could easily be (and often are) confused.

The authors give numerous practical tips, with reference to real-life problems that they explain in detail. Especially helpful is the authors' practice of treating several variations of a single problem, such as: "Here's how we'd analyze the data if we knew X and Y; later, we'll treat the case where we have to estimate X; finally, we'll treat a general case where we must estimate both."

Highly recommended, both for its content and as an example of how to teach a subject that's unfamiliar to most readers.

1 of 2 people found the following review helpful

5.0 out of 5 stars nice book March 30, 2012

By Riverside Zen

Format:Paperback

This book addresses the data analysis in a very friendly way, and maintains the high quality of academic monograph. The authors are the leading experts in statistics, and they contributed a lot of innovations in theoretical and in practical aspects.

0 of 8 people found the following review helpful

1.0 out of 5 stars it's so so February 12, 2013

By Shi Huang

Format:Paperback|Amazon Verified Purchase

When I tried to learn anything new, I like to try example by myself so that I can see how the theorem is applied.
but this book is not much examples.
too many theorem.
good for advanced people to read.
not good for beginner.