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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 (2011)

di Sharon Bertsch McGrayne

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5772040,907 (3.53)6
"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. 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, even 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"--… (altro)
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Unfortunately, very disappointing. I didn't really need to hear 10 different stories that said Bayes could solve x, without any of them really explaining how. ( )
  danielskatz | Dec 26, 2023 |
Bayes theory is cute. Pop nonfiction math books seem incapable of being patronizing on one extreme or invoking their math theorem as an abstract magical spell on the other. I prefer the later, which is what this is. How did we find Russian submarines? We cast Bayes at them. Sometimes, even as someone very familiar with Bayes theorem I found these invocations impossible to understand what was literally happening, but overall, this is an easy and mathy read. 3.5 stars. ( )
  settingshadow | Aug 19, 2023 |
Bayes is a statistical technique for estimating probability that starts off with a guess as an initial condition. This guess has brought it a lot of flack since it was invented in about 1760 from scientists and mathematicians who find the guess unscientific. For most of the 250 years since then it has been niche technique, not quite acceptable in polite mathematical circles, if not provoking outright hostility. However, its influence has grown hugely since the advent of computers which make the enormous calculations it requires practical. Surprisingly gripping yarn and very approachable. Recommended. ( )
  Matt_B | Mar 1, 2022 |
I came to this book hoping to understand what the heck scientists mean when they say they use a Bayesian approach or Bayesian statistical analysis, but without having to decipher too many formulas or greek letters. However, the book may have erred too much on the side of popular nonfiction; I'm surprised that after reading, I only have a slightly better understanding of Bayes than before.

But I can't exactly fault the author. I doubt there is much market for a popular explanation of Bayesian statistics, and it is a more intriguing and sellable book to chart the origins and many different applications of Bayes. There is this nagging feeling on my part that the book lacks grounding on some level...so many things are described as Bayesian, but the nitty gritty details of each problem that would help us really see them as such are missing.

The book also seems to end rather abruptly and without conclusion. We finally come to the flourishing of Bayesianism in the latter portion of the 20th century, and the advent of more powerful computers that help crunch the numbers, but the author treats it all rather cursorily, passing quickly from example to example.

Still, I found the book fairly interesting. I would also recommend David Salsburg's The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century for those looking for another well-written popular take on statistics. ( )
  stevepilsner | Jan 3, 2022 |
With The Theory That Would Not Die Sharon Bertsch Mcgrayne takes the reader on an adventurous romp through the history of Bayesian Analysis. From the initial founding by Thomas Bayes to its refinement by Robert Price and perfection by that Master of Mathematics Pierre-Simon Laplace, we explore its uses and abuses by many different people.

Having been shelved several times, it is surprising that one can even trace Bayes Rule to a single person, but it is the case here. Although the Frequentists of the past would have several complaints about its use in many different situations, there is no denying the utility of Bayes’ Rule. The primary reason against its use is the fact that initially, you have to make a guess on the probability of something. However, the power of Bayes is the fact that you are able to refine your guess as new information comes in.

As I mentioned, Bayes Rule was invented or first used by a man named Thomas Bayes. He invented it to find the probability of something that was unknown with limited information. Since he was a devout man and a theologian, that practical use turned out to be some sort of proof of God. After Bayes’ death, Price worked to make it more rigorous and mathematical. After that, Laplace developed it independently since he was such a genius, but acknowledged Bayes once he found out about him. Once Laplace died, no one used Bayes for a while since Laplace made it somewhat confusing. It didn’t help that Laplace was such a genius that he could skip several important steps when doing the math, preferring instead to put things like “this much should be obvious.”

In the realm of Statistics, Bayes was maligned and in many ways vilified. People made it their lifelong quest to call Bayes Theorem ridiculous and silly. This was mainly because you had to make an initial guess and that was not considered scientific. Eventually, it was used for all sorts of things. The main thing it was used for was insurance. If you were an actuary in the 1930s you had to figure out how to apply a probability to something that had not happened yet. So they used Bayesian Analysis to figure out insurance tables. As I said, the beauty of Bayes is that it grows more and more accurate as data accumulates. With Birth and Death records, you could find the probability of a ton of things. World War II was another time for Bayes to shine, but it happened with utmost secrecy. This is because it was used to break the German Enigma codes and end the war sooner.

So in the more modern times following WWII, it has been used for many different things. For instance, back in the 1950s or so, it was discovered that incidents of Lung Cancer and Heart Disease were on the rise. Using Bayes Rule, Dr Jerome Cornfield connected this rise to smoking cigarettes. Armed with Bayes Rule, he made many other earthshaking discoveries. Dr Cornfield was the one who made the connection between birth defects and thalidomide, the anti-nausea drug. In the 1960s, more people were driving and more airplanes were flying. So someone asked an actuary “what are the chances of two planes colliding midair?” Again, with the traditional realm of Frequentist statistics that question wouldn’t have made any sense. You would have to have something that happened in order to count it and get statistics from that. However, no one wants to frequently crash a plane into another plane to get data, so another method was used.

In any case, this book was really interesting. I have the version that contains a new preface, an epilogue, and a series of case studies. For instance, if you are a woman and get a positive mammogram, what are the chances you have Breast Cancer given that you have no family history or any other negative signs? The chances are pretty low actually. As it turns out, since that particular cancer is rare, it is more probable that you received a false positive. ( )
  Floyd3345 | Jun 15, 2019 |
The book by sharon bertsch mcgrayne, is about Bayes’ theorem stripped off the math associated with it. In today’s world, statistics even at a rudimentary level of analysis (not referring to research but preliminary analysis) comprises forming a prior and improving it based on the data one gets to see. In one sense modern statistics takes for granted that one starts off with a set of beliefs and improves the beliefs based on the data. When this sort of technique or thinking was first introduced, it was considered equivalent to pseudo-science or may be voodoo science. During 1700s when Bayes’ theorem came to everybody’s notice, Science was considered extremely objective, rational and all the words that go with it. On the other hand, Bayes’ was talking about beliefs and improving the beliefs based on data. So, how did the world come to accept this perspective of thinking? In today’s world, there is not a single domain that is untouched by Bayesian Statistics. In finance and technology specifically, Google Search engine algos + Gmail spam filtering , Net flix recommendation service in e-tailing space , Amazon book recommendations, Black-Litterman model in finance, Arbitrage models based on Bayesian Econometrics etc. are some of the innumerable areas where Bayes’ philosophy is applied. Carol Alexander in her book on Risk Management says that, world needs Bayesian Risk Managers and remarks that , “Sadly most of risk management that is done is frequentist in nature” .

You pick any book on Bayes and the first thing that you end up reading is about prior and posterior distributions. There is no history about Bayes that is mentioned in many books. It is this void that the book aims to fill and it does so with a fantastic narrative about the people who rallied for and against a method that took 200 years to get vindicated. Let me summarize the five parts of the book. This is probably the lengthiest post I have ever written for a non-fiction book, the reason being , I would be referring to this summary from time to time as I hope to apply Bayes’ at my work place. . . .
aggiunto da PLReader | modificaRK Book Reviews, Safeisrisky (Aug 13, 2011)
 
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"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. 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, even 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"--

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