Data Analysis: A Bayesian Tutorial (Oxford Science Publications)

by D. S. Sivia (Author)

8 customer reviews

ISBN-13: 978-0198518891

ISBN-10: 0198518897

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This is the first book on the maximum entropy and Bayesian methods aimed at senior undergraduates in science and engineering. It takes the mystery out of statistics by showing how a few fundamental rules can be used to tackle a wide variety of problems in data analysis. 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. As a logical and unified approach to the subject of data analysis, with a self-contained tutorial approach, this work will be valued by instructors and students alike.

Data Analysis: A Bayesian Tutorial (Oxford Science Publications)

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Product Details

Series: Oxford Science Publications
Paperback: 208 pages
Publisher: Oxford University Press (September 26, 1996)
Language: English
ISBN-10: 0198518897
ISBN-13: 978-0198518891
Product Dimensions: 6.1 x 0.3 x 9.2 inches
Shipping Weight: 10.6 ounces
Average Customer Review: 4.1 out of 5 stars See all reviews (8 customer reviews)
Amazon Best Sellers Rank: #1,166,820 in Books (See Top 100 in Books)
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Top Customer Reviews

Self-contained and readable tutorial guide

ByJuergen Kahrson April 2, 2000

Format: Paperback

Mathematics looks like a pile of abstract facts, axioms and theoremsto most people. It is hard to imagine that in some branches of mathematics, there are unsettled controversies about the meanings of basic notions like probability. Statistics is one of these branches, where professional researchers and lecturers can be divided into some sort of "schools of thought".
This small book of 189 pages is a tutorial introduction into statistics. It addresses senior undergraduates and research students in science and engineering. If symbols like integrals, factorials or notions like Eigenvalues do frighten you, you should first complete some courses on calculus and algebra before reading this book. Contrary to "classic" text books on statistics, this book employs the so called Bayesian understanding of probability. While the classic understanding of probability sees each probability as a long-run relative frequency, the Bayesian school sees it as a degree-of-belief (or plausibility). This may sound like a minor disagreement, but it leads to very different ways of solving problems.
Throughout the book, the author explains seven examples of increasing complexity to the reader and solves the problems. Especially in the first two chapters, he simplifies his favourite applications of probability theory in order to explain basic concepts like probability, the error-bar, correlation, and marginal distributions. Each of the graphical panels is explained in detail to make it easier to understand the intuitive meaning of concepts like the probability density function. Often, the author also mentions common misconceptions and vividly explains the consequences of such misunderstandings.
Having read this book, you will be able to employ probability theory in scientific and engineering work. For example in estimation of a parameter like a scattering angle. While these results are often very useful in practice, you should be warned that the Bayesian approach might annoy some representatives of the orthodox statistical guild.
Nevertheless, the book is a good tutorial which is worth reading.

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This is how a statistics book ought to be written!

ByPieter van Gelderon October 26, 2000

Format: Paperback

Sivia shows in the first part of his compact book (189 pages) very nice examples (such as the lighthouse problem, signal amplitudes in presence of background noise, etc) how the Bayesian theory works out. The kangaroo problem and monkey argument come up to explain the maximum entropy theory. Further on in the book examples are given in the area of DSP (digital signal processing) and on experimental design, added with references to Sivia's Bayesian applications in molecular spectroscopy, neutron scattering - and powder diffrication analysis. As an applied statistician within the area of hydrological engineering (flood frequency analysis), it was very fruitful to read Sivia's book to fresh up the way of thinking... I highly recommend the book to other applied statisticians!

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Learn what it means to be a "Bayesian"

ByK. Huyseron September 14, 2004

Format: Paperback

For years I listened to people present "Bayesian" solutions to problems without appreciating the subtler implications of the term. Bayes' theorem is one of the first topics taught in freshman-level probability and statistics. It's taught, and it's used, but it isn't a central part of the teaching of modern statistics.

Bayesians make it central. Sivia does a masterful job of deriving most of statistics from judicious applications of Bayes' theorem. He can do this, in part, because the visible universe is finite. Infinities and limit theorems can be bypassed, and previously impossible functional forms become workable.

The book is a tutorial; you have to think. But it's well worth it.

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A gem.

ByJohannes Soedingon December 4, 2005

Format: Paperback

This tutorial on Bayesian data analysis is a gem: very terse, yet explaining the concepts very clearly, giving many insightful examples along the way. This is achieved within only 180 pages by focussing on understanding and intuition instead of mathematical formalism. After reading this tutorial, the reader will be familiar with the way of thinking in Bayesian statistics. The tutorial thus encourages the reader to get more independent from the (conceptually more complicated) cook book statistics with the associated risk of misusage. When reading this book I felt as if a whole jumble of more or less unconnected pieces of statistical wisdom was finally falling into place within the Bayesian framework.

A few critical remarks: (1) A clearer structure with more informative section and subsection headings would help to quicker find things and keep the material orderly in one`s mind. (As an example, the two core chapters are entitled Parameter estimation I" and Parameter estimation II"). (2) The chapter on non-paramteric estimation is much harder to understand than the first six chapters. This is in part justified by the advancedness of the topic but it could profit from a streamlining (and updating). (3) This book certainly would have the chance to become much more popular than it is now if it was more reasonably priced.

The reader should have a firm command of elementary probability theory, first year calculus (Taylor expansion, multidimensional integration, finding the maximum of a multi-variable function), as well as elementary linear algebra (diagonalization, eigenvectors, determinants). Ideally, she should be familiar with basic classical statistics, as this will make her appreciate the elegance of the Bayesian view more. Physicists will love this book.

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Bayes' Theorem made simple

ByAnkson October 1, 2004

Format: Paperback

This is an excellent tutorial for the both the beginner (undergraduate) and more advanced scientist. Sivia takes the reader through several examples with simple and concise explanations. I have used many of the examples discussed in the book as starting points for problems that I have encountered in my work. I would recommend giving it a try...

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It is a good book, however

ByJ. MEJIA Muñozon May 15, 2016

Format: PaperbackVerified Purchase

It is a good book, however, it is no an easy tutorial to follow. The book omits a lot of details in the sake of simplicity, making it hard to follow. You need a strong background on statistics, specially in estimation theory, to read this book.

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Most Recent Customer Reviews

clear and concise

Sivia offers a brief but thorough explanation of how to use Bayesians in data analysis. He illustrates with important examples that commonly often arise in the sciences.Read more

Published on March 5, 2006 by W Boudville

poor pedagogy

Maybe it's just me but I found this book not very helpful. The easy stuff is repeated often (Bayes's theorem is quoted every few pages) but when a difficulty arises it is glossed...Read more

Published on January 16, 2004 by Ken Braithwaite

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