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Bayes' Rule: A Tutorial Introduction to Bayesian Analysis Paperback – 4 Jun 2013

4.8 out of 5 stars 18 customer reviews

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Product details

  • Paperback: 180 pages
  • Publisher: Sebtel Press; 1st edition (4 Jun. 2013)
  • Language: English
  • ISBN-10: 0956372848
  • ISBN-13: 978-0956372840
  • Product Dimensions: 15.2 x 1.1 x 22.9 cm
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (18 customer reviews)
  • Amazon Bestsellers Rank: 23,626 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

Review

"A crackingly clear tutorial for beginners. Exactly the sort of book required for those taking their first steps in Bayesian analysis." Dr Paul A. Warren. School of Psychological Sciences, University of Manchester. "This book is short and eminently readable. It introduces the Bayesian approach to addressing statistical issues without using any advanced mathematics, which should make it accessible to students from a wide range of backgrounds, including biological and social sciences." Dr Devinder Sivia. Lecturer in Mathematics, St John's College, Oxford University, and author of Data Analysis: A Bayesian Tutorial. "For those with a limited mathematical background, Stone's book provides an ideal introduction to the main concepts of Bayesian analysis. " Dr Peter M Lee. Department of Mathematics, University of York. Author of Bayesian Statistics: An Introduction. "Bayesian analysis involves concepts which can be hard for the uninitiated to grasp. Stone's patient pedagogy and gentle examples convey these concepts with uncommon lucidity. " Dr Charles Fox. Department of Computer Science, University of Sheffield.

Review

Editorial Reviews

Review

"An excellent book ... highly recommended. "
CHOICE: Academic Reviews Online, February 2014.

"Short, interesting, and very easy to read, Bayes' Rule serves as an excellent primer for students and professionals ... "
Top Ten Math Books On Bayesian Analysis, July 2014.

"An excellent first step for readers with little background in the topic. "
Computing Reviews, June 2014

"An accessible introduction to Bayesian analysis for those with little mathematical experience."
Journal of the Royal Statistical Society, 2015.

From the Back Cover


"Bayes' Rule explains in a very easy to follow manner the basics of Bayesian analysis." Dr Inigo Arregui, 
Ramon y Cajal Researcher, Institute of Astrophysics, Spain.


"A crackingly clear tutorial for beginners. Exactly the sort of book required for those taking their first steps in Bayesian analysis."
Dr Paul A. Warren.
School of Psychological Sciences, University of Manchester.

"This book is short and eminently readable. It introduces the Bayesian approach to addressing statistical issues without using any advanced mathematics, which should make it accessible to students from a wide range of backgrounds, including biological and social sciences."
Dr Devinder Sivia.
Lecturer in Mathematics, St John's College, Oxford University, and author of Data Analysis: A Bayesian Tutorial.

"For those with a limited mathematical background, Stone's book provides an ideal introduction to the main concepts of Bayesian analysis. "
Dr Peter M Lee.
Department of Mathematics, University of York. Author of Bayesian Statistics: An Introduction.

"Bayesian analysis involves concepts which can be hard for the uninitiated to grasp. Stone's patient pedagogy and gentle examples convey these concepts with uncommon lucidity. "
Dr Charles Fox.
Department of Computer Science, University of Sheffield.

See all Product Description

Customer Reviews

4.8 out of 5 stars
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Top Customer Reviews

Format: Paperback Verified Purchase
Until recently, many texts on Bayesian inference assumed the reader had a strong background in mathematics or statistics. I found that really frustrating and it really got in my way of understanding this stuff. But this concise book (~160 pages) is a really great introduction. If I had this book when I was learning, then my journey would have been much easier.

Rather than diving directly into things, Chapter 1 provides a range of examples that demonstrate some of the core concepts. I think this is really important because often people are coming from a frequentist background, and unless certain key conceptual shifts are made, then it's tricky to gain traction. Chapter 7 (Bayesian Wars) deals with this aspect as well, so I felt it might be better coming after Chapter 1.

I think the topic coverage is great for an introductory book. It will get the reader familiar with the workings of a lot of basic problems/models, which provides an excellent foundation for going on to more elaborate situations such as hierarchical inference or model comparison.

The inclusion of an Appendix of mathematical notation was very useful.

Highly recommended. One of the best introductions to the nuts and bolts of Bayesian inference for non-statisticians.

I'll make some humble suggestions for improvement if a second edition were to emerge:
1. While the book does include some Matlab code, it might be worth including more snippets of code in the book (or even just in Appendices). When it boils down to it, people will be coding up the maths, and readers are likely to have a degree of programming experience. Including code snippets alongside the maths may help bridge the gulf between mathematical understanding and Matlab implementation.
2.
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Very good introduction, easy to follow, explains the concepts in a simple systematic way. Obviously paying close to 20 pounds for 130 pages of text (this is the number if you exclude appendices) is a very high price, but if you are looking somewhere to start with Bayes, this is quite possibly the place.
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As a psychology / stats novice I have found this book very useful as an introduction to Bayesian processing. The combined examples of medical statistics versus the probability of flipping a coin give two different approaches to Baye's rule which are helpful and easy to follow. 5*'s.
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Bayesian statist ics are hard to get your head around because they incorporate your own ignorance in the calculation.This book offers as simplke an account as is possible and some useful model calculations.
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Very clear and exceptionally well written introduction to the subject with easy to follow examples and a logical sequence. Nice discussion at the end explaining the emotion and prejudice that once dogged this theory, which is fully covered in Sharon Bertsch McGrayne's complementary book, The theory that would not die, which doesn't do the maths very well. A big thank you James Stone.
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This book had me hooked. It attempts to explain Bayes theorem as intuitively as possible, introducing only the necessary math and showing how this math makes your life easier rather than harder. The first chapter is entirely practical examples and gave me the drive to read the rest of it.
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As an introductory book on Baye's Rule, I found this book to be an excellent choice.
It is very clear, using many diagrams, graphs, tables, simple equations & not just text to elucidate the subject.
The subject is introduced in a "Bottom up" approach, so does not blind the reader with advanced theorems from the outset, I thoroughly recommend
this book.
Any mathematical formula are introduced on a "need to know" basis, so readers do not have to be mathematicians to grasp the contents.
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Format: Paperback
Of all the areas of mathematics, probability is arguably the most intriguing to the non-mathematician, and this is particularly the case with Bayesian analysis, which can be delightfully counter-intuitive. However, the more complex aspects can be tricky to get your head around, so I was delighted to have the chance to read this book, subtitled 'a tutorial introduction to Bayesian analysis.'

I need to say straight away that this isn't really a popular science title, and the author is very clear about this - it's a kind of textbook lite - but I was specifically sent this book to see how it would work for the general popular science reader, and that is reflected in its three stars. If you have found out a bit about Bayes this book is an opportunity to dive into it a little deeper without taking on the full rigour of a textbook approach. Why should you care? Bayes gives us a mechanism that enables us to do things like go from a known piece of information like 'what's the probability of a symptom given a disease' to estimate a much more interesting unknown like 'what's the probability of the disease given a symptom' - an extremely powerful mechanism.

James Stone does his best to accommodate us ordinary folk. The book opens well, apart from a bizarrely heavy smattering of references on page 1, with a gentle introduction, and keeps the mood light after the classic disease application by looking for a mechanism of determining whether some said 'four candles' or 'fork handles' in the Two Ronnies style. If you are prepared to make an effort, for most of us probably a considerable effort, you will go on to pick up a lot more about using Bayes than you already knew (if you aren't a mathematician).
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