PDF Ebook , by Sharon Bertsch McGrayne
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, by Sharon Bertsch McGrayne
PDF Ebook , by Sharon Bertsch McGrayne
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Product details
File Size: 1225 KB
Print Length: 335 pages
Publisher: Yale University Press (May 17, 2011)
Publication Date: May 17, 2011
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B0050QB3EQ
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#142,953 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
"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.
This book blew my mind. Most of the examples used and the mathematicians involved I was familiar with, but not the Bayesian angle. I feel like the wool was pulled away from my eyes after reading this book. Other reviews complain about the lack of math in the main text of the book, but I disagree. One I think anyone reading this book knows Bayes Rule and two I think the actual math would get in the way of the story. One of the biggest themes of the book is that Bayes is about practical problem solving and that once computers arrived on scene to allow for the iterative brute force solving that it really took off. The process or way of thinking is made clear in the text. Really it was great read, I found myself texting people while reading saying did you know this was Bayes, over and over.
The historical anecdotes in the book might be interesting to people who use/read statistical analyses in their work as I do. However, don't read this book if you expect to learn how to actually apply Baye's rule or conditional probability. I might have given this book 3 stars but there is a terrible error in the appendix explaining how to apply Baye's rule to breast cancer test results. THE ONLY CONCRETE EXAMPLE IN THE BOOK IS WRONG! What a disappointment. And for those of you who are confused by the results in the appendix: P(B|A) = 32/40 not 32/10000.
The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy is a non-technical book that deals with the Baysian Statistics.Thomas Bayes (1701–1761) was a Scottish clergyman who developed the technique. Basically, Bayesian statistics is a set of mathematical formulas where “one's inferences about parameters or hypotheses are updated as evidence accumulates.†Simply put, Bayes allows for our subjective inferences as the starting point of inquiry. Then, with accumulated evidence through testing, those initial assumptions are refined.This sounds a great deal like our common sense approach to life, and it is. We all make hunches about probable outcomes of future events based on incomplete current information, and then change and alter our assumptions based on the results.This book walks a fine line between a technical exposition of Bayesian statistics and a popular one. It does this to the point where I think many readers will feel like they are missing something --- as if the surface is only being skimmed. But the author had no choice; otherwise, the book would have gotten bogged down in technical details most readers can’t understand. So, this book has a fair balance between the two… if not somewhat thin in math while being thick in history!
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