Download 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 fb2

by Sharon Bertsch McGrayne

Sharon_Bertsch_McGrayne Mathematics English

Download 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 fb2

Mathematics

Author:

Sharon Bertsch McGrayne
ISBN:

0300188226
ISBN13:

978-0300188226
Genre:

Science / Math
Publisher:

Yale University Press; 37310th edition (September 25, 2012)
Pages:

360 pages
Subcategory:

Mathematics
Language:

English
FB2 format

1703 kb
ePUB format

1109 kb
DJVU format

1320 kb
Rating:

4.2
Votes:

929
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A book simply highlighting the astonishing 200 year controversy over Bayesian .

A book simply highlighting the astonishing 200 year controversy over Bayesian analysis would have been highly welcome. 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. 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.

emerged triumphant from two centuries of controversy

The theory that would not die : how Bayes' rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security. Enlightenment and the anti-Bayesian reaction. Causes in the air ; The man who did everything ; Many doubts, few defenders - Second World War era.

Start by marking The Theory That Would Not Die . Anyway, the annoyance of this book is the author pontificates on how great Bayes' Rule is without ever, you know, actually giving us a mathematical example.

Start by marking 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 as Want to Read: Want to Read savin. ant to Read. The chapters basically read like this.

McGrayne, Sharon Bertsch (2011). This is the most rudimentary version of Bayes’ Rule, showing how we can reverse. the direction of the inference. The Theory That Would Not Die: How Bayes’. Rule Cracked the Enigma Code, Hunted Down Russian Submarines & Emerged Tri-. umphant from Two Centuries of Controversy.

Sharon Bertsch Mcgrayne. eISBN: 978-0-300-17509-7. Subjects: History of Science & Technology, Mathematics.

Mobile version (beta). McGrayne, Sharon Bertsch. Download (pdf, . 4 Mb) Donate Read. Epub FB2 mobi txt RTF. Converted file can differ from the original. If possible, download the file in its original format.

Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new .

Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.

She has an interesting story to tell about the last 250 years of Bayesian thinking, how the theory has developed, and its many applications including how to price insurance, how to aim artillery, how to break the Enigma code, who wrote The Federalist Papers, how to find Russian nuclear subs, how to estimate the probability of a shuttle disaster, when to do various.

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 by Sharon Bertsch McGrayne.

In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.

Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

Gann

"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.

Kendis

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.

Oreavi

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!

Buge

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.

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