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Data Analysis: A Bayesian Tutorial Paperback – July 27, 2006

ISBN-13: 000-0198568320 ISBN-10: 0198568320 Edition: 2nd

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Product Details

  • Paperback: 264 pages
  • Publisher: Oxford University Press; 2 edition (July 27, 2006)
  • Language: English
  • ISBN-10: 0198568320
  • ISBN-13: 978-0198568322
  • Product Dimensions: 9.2 x 0.8 x 6.1 inches
  • Shipping Weight: 14.9 ounces (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #159,884 in Books (See Top 100 in Books)

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 O.X11 5D.J. John Skilling Maximum Entropy Data Consultants 42 Southgate Street Bury St Edmonds Suffolk I.P33 2A.Z.

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Customer Reviews

4.6 out of 5 stars
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I highly recommend this book to anyone willing to learn about Bayesian statistic and applications.
Marcelo Chiapparini
Sivia and Skilling give a concise and clear exposition of Bayesian statistical analysis, and pair it with practical, real examples.
M. Schneider
A good companion to other "classical" Bayesian statistics books such as BDA by Gelman et al.
Flavio Nardi

Most Helpful Customer Reviews

51 of 54 people found the following review helpful By L. Antoine on July 7, 2008
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 !
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36 of 38 people found the following review helpful By M. Schneider on August 3, 2007
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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!
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38 of 41 people found the following review helpful By FG on May 18, 2008
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
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11 of 12 people found the following review helpful By Kevin Knuth on August 15, 2013
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As a physics student I was frustrated by statistics with its apparent lack of conceptual foundation and the toolbox approach to data analysis. A little more than 15 years ago, I picked up the first edition of this book and learned Bayesian data analysis from it. The topic is introduced from a practical perspective designed for someone who wants to use these methods for data analysis applied to real problems. This relatively small book clearly, cogently, and pleasantly covers the concepts, the theory and practice. I was pleased to be able to use this text to guide me in applying Bayesian data analysis methods to my own problems. Today, as an experienced practitioner, I find myself still referring to it.

For the last seven years, I have taught an upper level undergraduate/graduate level course on Bayesian Data Analysis in the physics and computer science departments at the University at Albany (SUNY). This text is required reading, and I find the students to be more than grateful for it. It is perfect for someone who wants to hit the ground running in applying these methods to real problems.

This book is extremely valuable. I most highly recommend it!
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8 of 9 people found the following review helpful By Pdecordoba on October 22, 2012
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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.
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2 of 2 people found the following review helpful By W. Murphy on January 19, 2014
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A friend of mine introduced me to Bayesian analysis as a framework for handling the acoustic analysis problems which we deal with. He recommended this text as a good introduction to the theory and he is correct. I am working my way through the text and am trying to implement the exploration of the parameter spaces that must be explored. The book does not have code to help you get started, but that was not my purpose for getting the book. Sivia provides a very readable and comprehensive explanation of the Bayesian methods.
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2 of 2 people found the following review helpful By Joseph M. Powers on September 6, 2013
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I really learned to appreciate Bayesian statistics by working the insightful example problems provided in the first few sections of the book. Read Jaynes for his argumentation and philosophical underpinnings. Read Sivia to jump start your inner Bayesian.
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