Data Analysis: A Bayesian Tutorial (Oxford Science Publications)

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Summary: Comments received/found on my Bayesian tutorial book
Review: As planned, I did make it to my office last week, and sure enough, there was your book waiting for me on my desk. I have now had a chance to go through it lightly, and think it is great; just what has been needed. So I have put a reference to your book in my very long list of references, with a comment that it should be considered an adjunct to my work, supplying the necessary numerical and practical stuff that I cannot take time for in my own book. Congratulations! (Ed Jaynes, Wayman Crow Professor of Physics, Washington University, St. Louis: 9th October, 1996)

Very clearly written tutorial on the ideas behind Bayesian inference. (Prof. Padhraic Smyth, Information & Computer Science, UC Irvine)

An excellent and clear description of the role of Bayes's theorem in scientific research. (Prof. Charles Carter, Biochemistry & Biophysics, UNC, Chapel Hill)

There are many introductory texts on BPT, an excellent one has been written by Sivia. (Dr. Michael Kelly, Dept. of Physics, University of Michigan)

I would first like to take the opportunity to sincerely thank you for the extraordinary clarity with which you did write your book. It is indeed rare to find books that focus on the explanation of concepts. (Dr. Ignace Lasters, PE GenScope, Belgium)

I recently had the pleasure of reading your textbook "Data Analysis: a Bayesian tutorial" which was recommended to me by a colleague. I must first commend you on the clarity and elegance of the work. (Christopher Brown, Dept. of Chemistry, Dalhousie University, Canada)

Devinder Sivia presents a very straightforward account of Bayesian analysis. Rather than going for mathematical rigor and an axiomatic approach, he introduces concepts as they are needed to solve a sequence of increasingly complex analysis problems. Very readable and informative. (Dr. Ken Hanson, Los Alamos National Laboratory, New Mexico, USA)

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Summary: Learn what it means to be a "Bayesian"
Review: 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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Summary: Self-contained and readable tutorial guide
Review: 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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Summary: poor pedagogy
Review: 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 over. Maybe it gets better: I decided not to finish the book.

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Summary: This is how a statistics book ought to be written!
Review: 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!

Rating:
Summary: This is how a statistics book ought to be written!
Review: 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!

Rating:
Summary: Bayes' Theorem made simple
Review: 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...