Salon Beauty Summer Reading Introducing Prime Wardrobe nav_sap_plcc_ascpsc Unlimited Music. Always ad-free. Learn more. Introducing Fire TV Cube Grocery Handmade Personalized Jewelry Book a house cleaner for 2 or more hours on Amazon TheGrandTour TheGrandTour TheGrandTour  Echo Fire tablets: Designed for entertainment Kindle Paperwhite GNO Shop now STL18_GNO

Customer Review

on May 29, 2011
"The Theory That Would Not Die" is an enjoyable account of the history of Bayesian statistics from Thomas Bayes's first idea to the ultimate (near-)triumph of Bayesian methods in modern statistics. As a statistically-oriented researcher and avowed Bayesian myself, I found that the book fills in details about the personalities, battles, and tempestuous history of the concepts.

If you are generally familiar with the concept of Bayes' rule and the fundamental technical debate with frequentist theory, then I can wholeheartedly recommend the book because it will deepen your understanding of the history. The main limitation occurs if you are *not* familiar with the statistical side of the debate but are a general popular science reader: the book refers obliquely to the fundamental problems but does not delve into enough technical depth to communicate the central elements of the debate.

I think McGrayne should have used a chapter very early in the book to illustrate the technical difference between the two theories -- not in terms of mathematics or detailed equations, but in terms of a practical question that would show how the Bayesian approach can answer questions that traditional statistics cannot. In many cases in McGrayne's book, we find assertions that the Bayesian methods yielded better answers in one situation or another, but the underlying intuition about *why* or *how* is missing. The Bayesian literature is full of such examples that could be easily explained.

A good example occurs on p. 1 of ET Jaynes's Probability Theory: I observe someone climbing out a window in the middle of the night carrying a bag over the shoulder and running away. Question: is it likely that this person is a burgler? A traditional statistical analysis can give no answer, because no hypothesis can be rejected with observation of only one case. A Bayesian analysis, however, can use prior information (e.g., the prior knowledge that people rarely climb out wndows in the middle of the night) to yield both a technically correct answer and one that obviously is in better, common-sense alignment with the kinds of judgments we all make.

If the present book included a bit more detail to show exactly how this occurs and why the difference arises, I think it would be substantially more powerful for a general audience.

In conclusion: a good and entertaining book, although if you know nothing about the underlying debate, it may leave you wishing for more detail and concrete examples. If you already understand the technical side in some depth and can fill in the missing detail, then it will be purely enjoyable and you will learn much about the back history of the competing approaches to statistics.
297 people found this helpful
|1111 comments|Report abuse| Permalink

Product Details

3.9 out of 5 stars