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Data Analysis: A Bayesian Tutorial (Englisch) Taschenbuch – 27. Juli 2006


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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

Synopsis

Sivia (St. Catherine's College) and Skilling, a data consultant, offer a unified approach to the study of data analysis. The 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, the book illustrates their use with a rang

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Amazon.com: 14 Rezensionen
50 von 53 Kunden fanden die folgende Rezension hilfreich
good 7. Juli 2008
Von L. Antoine - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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 !
36 von 38 Kunden fanden die folgende Rezension hilfreich
In-Depth and Practical 3. August 2007
Von M. Schneider - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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!
38 von 41 Kunden fanden die folgende Rezension hilfreich
Very good introduction 19. Mai 2008
Von FG - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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
11 von 12 Kunden fanden die folgende Rezension hilfreich
Not only do I recommend it... I REQUIRE IT 15. August 2013
Von Kevin Knuth - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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!
7 von 8 Kunden fanden die folgende Rezension hilfreich
Extremely useful 23. Oktober 2012
Von Pdecordoba - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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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