Amazon.com: Customer Reviews: Data Analysis: A Bayesian Tutorial
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Customer Reviews

4.5 out of 5 stars
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on July 7, 2008
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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on August 3, 2007
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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on May 18, 2008
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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on August 15, 2013
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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on September 6, 2013
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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on January 19, 2014
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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on October 22, 2012
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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on April 3, 2016
Very nice book. Fills a need for a concise 'basic' introduction to Bayesian learning. Very similar in scope to Abu Mostafa's book learning from data, to the book of Hastie, Tibshirani, James and Witten intro to statistical learning, or to Andrew Ng's Coursera machine learning intro course. Lots more math than those courses but that's to be expected for a book on Bayesian learning. There are lots of fully worked out examples in this book. The amount of math may make it hard for the less mathematically inclined reader to follow the examples. People have lots of good things to say about Kruschke's Bayesian statistics book but that one is too long to be considered a 'short course'.
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on April 3, 2014
Solid introduction to Bayesian statistics with several examples from the physical sciences. This very well written text is self contained. The Bayesian method is motivated from first principles and basic probability. A good companion to other "classical" Bayesian statistics books such as BDA by Gelman et al.
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on October 30, 2012
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.
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