- Paperback: 264 pages
- Publisher: Oxford University Press; Second Edition edition (Jun 1 2006)
- Language: English
- ISBN-10: 0198568320
- ISBN-13: 978-0198568322
- Product Dimensions: 23.2 x 19.2 x 1.4 cm
- Shipping Weight: 422 g
- Amazon Bestsellers Rank: #124,145 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Details |
Product Description
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
`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
Book Description
This title includes the following features: An easy to read tutorial introduction to data anlaysis.; Concise, being one of the slimmest books in the field!; Self-contained - assumes little or no previous statistical training.; Good illustrative examples where the basic concepts are explained with a series of examples that become progressively more advanced, but that are always kept as simple as possible to aid understanding.; A contribution from John Skilling, an expert in numerical techniques. He introduces the simple but powerful new 'nested sampling' technique for Bayesian computaton.
Inside This Book
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4.8 out of 5 stars (4 customer reviews)
29 of 30 people found the following review helpful
4.0 out of 5 stars
good, July 7 2008
By L. Antoine - Published on Amazon.com
This review is from: Data Analysis: A Bayesian Tutorial (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 !
26 of 27 people found the following review helpful
5.0 out of 5 stars
In-Depth and Practical, Aug 3 2007
By M. Schneider - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Data Analysis: A Bayesian Tutorial (Paperback)
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!
22 of 23 people found the following review helpful
5.0 out of 5 stars
Very good introduction, May 18 2008
By student - Published on Amazon.com
This review is from: Data Analysis: A Bayesian Tutorial (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
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