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Data Analysis: A Bayesian Tutorial
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'.
Customer Reviews:
good
By L. Antoine - July 7, 2008This 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 !
In-Depth and Practical
By M. Schneider - August 3, 2007Sivia 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!
Very good introduction
By FG - May 18, 2008This 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
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