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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
by
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 explo ...more
In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explo ...more
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Hardcover, 336 pages
Published
May 17th 2011
by Yale University Press
(first published May 14th 2011)
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Showing 1-30
If only I had known how to use Bayesian probabilities before reading this book I could have taken the probability of my liking a book well received in a NY Times Book Review as my prior, plugged that in to a Bayesian calculation as to whether or not I would like this book, and quickly would have come up with the answer "highly unlikely" and saved myself a few hours of my life.
According to the author, Bayes' rule is the greatest mathematical equation/formula/thought process in the history of hist ...more
According to the author, Bayes' rule is the greatest mathematical equation/formula/thought process in the history of hist ...more
I think this is the first book about Bayes' theorem and its applications, for the general reader. The book does not explicitly state the theorem as a mathematical formula, until the second appendix. However, the general idea is described, as well the general ideas behind it. The history of the theorem is described in some detail.
The ebb and flow in belief in the theorem over the course of 150 years is interesting. Applying Bayes theorem requires a prior probability, and this is often poorly know ...more
The ebb and flow in belief in the theorem over the course of 150 years is interesting. Applying Bayes theorem requires a prior probability, and this is often poorly know ...more
As someone who actually works with Bayesian methods, I was very much looking forward to reading this book. The strange history of Bayes' Theorem had been briefly mentioned in other, more technical books I had read. I finally wanted to get the whole story.
Alas, that story, at least as presented in this book, turned out to be not quite so exciting. Except for the insights into Laplace's involvement, and in particular the interesting sections on Alan Turing's work, I found this to be a rather lifel ...more
Alas, that story, at least as presented in this book, turned out to be not quite so exciting. Except for the insights into Laplace's involvement, and in particular the interesting sections on Alan Turing's work, I found this to be a rather lifel ...more
I read this book a long time ago but must have skimmed it then. I got a lot more out of it on this second reading. It is a history of Bayes theorem starting with Bayes himself and tracing the history of this idea from the mid-1700s to the present. It is a fascinating history at one end highlighting a philosophical dispute between frequentists and Bayesians or to oversimplify between an objective focus on probability vs. a more subjective epistemological view. At the other end is the nitty gritty
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I wish I had liked this book more than I actually did. Most of the stories reported are very interesting and entertaining, reflecting how academics have been fiercely debating conceptual aspects of Bayes theorem, as well as the bayesian-frequentist feud, while at the same time it was being successfully applied in many crucial issues such as finding stray atomic weapons and linking smoking to lung cancer.
However, I did not find this book well-written at all. It's just not an exciting read - and i ...more
However, I did not find this book well-written at all. It's just not an exciting read - and i ...more
Excellent and very readable book about the history of Bayes' theorem. I never realized that Bayesian statistics, one of the cornerstones of modern data science, had such a turbulent history--so turbulent that, during the cold war, being called a "bayesian" was tantamount to being called a Communist.
If you're at all interested in the history of mathematics, this is a surprisingly exciting story. I expected a rather dull and academic history; that is NOT what this book is.
If you're at all interested in the history of mathematics, this is a surprisingly exciting story. I expected a rather dull and academic history; that is NOT what this book is.
This was an excellent biography of Bayes' Rule, which basically glossed over Bayes himself. The author chose instead to examine the lesser known scientists and applications associated with Bayes. As a result, after reading this you are likely to call Bayes' Rule BLP Rule, for Bayes-Laplace Rule. The author was most interested in highlighting the work done by Pierre-Simone Laplace, who I feel I have come to know so much more after this biography. Prior to this book, Laplace came on my radar after
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Apr 19, 2014
Ms.pegasus
rated it
liked it
·
review of another edition
Recommends it for:
anyone who has read the Nate Silver book SIGNAL AND THE NOISE
Recommended to Ms.pegasus by:
citation if Nathan Silver's book
Shelves:
nonfiction,
history
As the subtitle proclaims, this book chronicles the history of science It also demonstrates how a simple formula evolved into a sophisticated application that required the invention of high speed computers to exploit its potential for prediction. It complements the information in Nate Silver's book, THE SIGNAL AND THE NOISE.
McGrayne introduces the reader to Bayes's Theorem with the proposal that given the unknown position of a billiard ball, its probable position can be narrowed by collecting da ...more
McGrayne introduces the reader to Bayes's Theorem with the proposal that given the unknown position of a billiard ball, its probable position can be narrowed by collecting da ...more
Bayes' Rule is a mathematical formula that allows one to calculate a conditional probability (such as the probability that a woman has breast cancer given that she has a postive mammogram). It has many useful attributes, such as allowing one to updates ones estimates of a probability as you obtain new information, and can be adapted to deal with such basically non-numerical forms of information as expert opinion. One can also use it to estimate the probability of events that have not happened,
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It is the third book I read about statistics in a short while and it is probably the strangest. After my dear Taleb and his The Black Swan, after the more classical "Naked Statistics: Stripping the Dread from the Data", here is the history of the Bayesian statistics. If you do not know about Bayes, let me just add that I like the beautiful and symmetric formula: [According to wikipedia] For proposition A and evidence B, P(A|B) P(B) = P(B|A) P(A) with P(A), the prior, is the initial degree of bel
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First, a cachet: unless you are already interested in one or more of Statistics, Decision Theory, Machine Learning, or the history and philosophy of Science and Mathematics, you are probably not a member of this book's audience. However, if you are, you will find a meticulously researched, erudite, and detailed survey of the history of statistics and decision theory. Undergraduate level familiarity with statistics and a generalist understanding of Bayes' rule would be very helpful but not critic
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It probably takes a special sort of person to dive into an entire book about one statistical theory, but for those so-motivated, this one pays off.
The pro's: The author has done a phenomenal job at capturing and richly detailing the very "large" personalities that have championed (or condemned) the use of Bayes' Rule through the centuries, amidst a little-known and long-simmering war that has persisted between statistical Bayesians and frequentists since the concept was first brought forward. T ...more
The pro's: The author has done a phenomenal job at capturing and richly detailing the very "large" personalities that have championed (or condemned) the use of Bayes' Rule through the centuries, amidst a little-known and long-simmering war that has persisted between statistical Bayesians and frequentists since the concept was first brought forward. T ...more
A friend recently pointed out that the term 'Bayesian' is now entering the common parlance such that the NY Times can use it in an article without explanation. This would come as a huge surprise and disappointment to many statisticians from the early part of the 20th century, when Bayesian was a bad word and the theory was largely refuted. Why would a statistical theory be so upsetting, you might ask...well, McGrayne's book explains why, and gives the history of the theory over time and how it m
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There is some very important information here but it is buried under a giant pile of whocares?.
By giving us the life of Bayes, the childhood of Laplace , ... , I think the author is trying to force the book to have a narrative, but I doubt that many people buying books about mathematical theories are interested in the minor details of the mathematicians' lives. This type of writing would be bad enough if the importance of Bayesian analysis were clearly explained, but it isn't. For instance, in ...more
By giving us the life of Bayes, the childhood of Laplace , ... , I think the author is trying to force the book to have a narrative, but I doubt that many people buying books about mathematical theories are interested in the minor details of the mathematicians' lives. This type of writing would be bad enough if the importance of Bayesian analysis were clearly explained, but it isn't. For instance, in ...more
My following review on "The Theory That would Not Die" is not just a review of the book, but also a detailed account of the actual Bayesian theory and its numerous benefits that unfortunately still remain hidden from general public. I hope that by sharing the importance of this great piece of theory it might kindle your interest to learn more and read this delightful book by Sharon Bertsch McGrayne. To understand the importance and the concept of Bayes Theorem it’s important to establish its nee
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Bayes (including MCMC) had been called “arguably the most powerful mechanism ever created for processing data and knowledge“
“The Theory That Would Not Die” is well written, informative and an engaging read. From its roots in the "principle of inverse probability”, Bayes rule and Laplacean probability now have numerous newer references and more nuanced meanings. Bayesian probabilities, Bayesian statistical inference (or more simply Bayesian inference), probability based statistics, probability of ...more
“The Theory That Would Not Die” is well written, informative and an engaging read. From its roots in the "principle of inverse probability”, Bayes rule and Laplacean probability now have numerous newer references and more nuanced meanings. Bayesian probabilities, Bayesian statistical inference (or more simply Bayesian inference), probability based statistics, probability of ...more
A truly detailed and fascinating history of Bayes theorem, a little exhausting in the later half because of the sheer amount of information in the book (it is a history book after all) but a small suggestion that worked for me - breeze over the historical details (seasoned readers would know what I'm talking about) and don't try to retain them - but pause and think over the interesting bits (like Bayes' first experiment) and make sure you understand the essential concept of the theorem in mathem
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I initially thought this book would be more scientific and talk about how Bayes Theorem helped solve various problems throughout history. However, this was more of a history book, which is not something that I have ever read as history just isn't something I've ever been interested in. Anyway, I understand that the book was on the side of Bayes' Theorem but I felt as though the author made those who study traditional statistics (not Bayesian stats) were the bad guys and she spent a lot of time,
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The basic idea of Bayes' Rule is that you treat probability as a degree of belief (i.e. how much are you willing to bet on something) instead of a relative frequency (i.e. count the number of royal flushes out of all possible poker hands).
Bayes' Rule allows you to "learn" by updating your (prior) degree of belief of something (i.e. probability of finding a sunken ship in a certain part of the ocean) given new information (i.e. a captain's log) in order to obtain knowledge in a "posterior" belie ...more
Bayes' Rule allows you to "learn" by updating your (prior) degree of belief of something (i.e. probability of finding a sunken ship in a certain part of the ocean) given new information (i.e. a captain's log) in order to obtain knowledge in a "posterior" belie ...more
Impressive research, an enjoyable read about the history of Bayesian statistics. However, lacks a good description about the basic question – what is Bayesian statistics. The book would benefit from a first chapter with a couple engaging examples of how Bayes works. Must be able to last through the first couple dry chapters.
Bayes helps real-life practitioners assess evidence, combine every possible form of information, and cope with the gaps and uncertainties in their knowledge. Some of Bayes’ ...more
Bayes helps real-life practitioners assess evidence, combine every possible form of information, and cope with the gaps and uncertainties in their knowledge. Some of Bayes’ ...more
Excellent. Sometimes the writing wasn't brilliant, but the history of Bayes theorem is fascinating. It was used to crack the Enigma code, locate lost nukes and subs, find survivors of boats lost at sea, identify the cause of lung cancer and is integral to online search/product suggestions.
Simply stated, it is that the probability of something can be predicted from whatever information we currently have when updated with incoming information. Because this is computationally intense, Bayes theorem ...more
Simply stated, it is that the probability of something can be predicted from whatever information we currently have when updated with incoming information. Because this is computationally intense, Bayes theorem ...more
I thought I would love this book -- I'm a Bayes enthusiast who has enjoyed the power of Bayesian machine learning. It was nice to know the history of how Bayes theorem developed, how many people contributed significantly to it (Laplace and others), and how often it got buried. And as I was describing to Leslie why I wanted to read this book, I told her about how I thought Bayes was much more how people work with science naturally, i.e. they form a belief before they know how things are, do exper
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I liked this book a lot more than I thought I would, particularly given its ratings. This is a non-technical history of the use of Bayesian statistics. The book is so non-technical that no background in statistics is really required at all. The non-technical nature also comes at the cost of a lack of detail. The book only explains WHY Bayesian methods were so poorly thought of for years at a very high level, without enough detail to really understand the underlying issues. In addition, its descr
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Because I had read several GoodReads reviews of The Theory That Would Not Die by Sharon Bertsch McGrayne, I knew that it was not an introduction to Bayesian statistics. I was still sufficiently intrigued by the subtitle How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy to take it out of the library. The book had lots of “who,” “what,” “when,” and “where,” but almost no “how.” I left with a knowledge who applied Bayes
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Light history of Bayesian statistics & related topics. I enjoyed the book a lot; McGrayne has a good eye for the amusing details, and she conveys at least some of the intuition (although some graphs or examples would have helped the reader - I liked the flipping coin illustrations in Dasivia 2006 _Bayesian Data Analysis_). It's also remarkably synoptic: I was repeatedly surprised by names popping up in the chronology, like BUGS, Bretthorst, Fisher's smoking papers, Diaconis, the actuarial us
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Incredibly entertaining and informative. The book deals not only with the history of statistics and computing (which I'd never think would be interesting), but more generally with fascinating European history, and -- fundamentally -- with the role of luck in life. Reading the vignettes of many statisticians, actuaries, scientists, professors, mathematicians, military officers, philosophers, etc. highlighted the uncertainty of our paths in life and the role of timing, luck, and just the regular c
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Complex and brainy, but very interesting; I think it's the best you can do at explaining what Bayes' rule is to non-specialists like me. With an admirable minimum of equations, McGrayne talks about the origin of the Bayes statistical method, the controversies around it, and its wide range of applications. I almost wish I could give it something more like 3 1/2 stars, because I feel like I should have come away from the book with a better ability to explain exactly what Bayes' rule is, rather tha
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I really wanted to like this book more than I did. The first three chapters are particularly frustrating -- I got the feeling that the author knew much more than she dared to say in a popular book and figuring out exactly what Bayes and Laplace did was not easy. The rest of the book, which consists primarily of descriptions of how Bayes' Rule has been applied, is much better. In particular, the chapters on how Bayesian methods were used to crack the Enigma code and find missing submarines were i
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This book is essentially about the history of big data. The idea that you can use lots of numbers and data to make accurate predictions about the current state (and even future) was a novel insight once. For many 100s of years, people agreed how to do it, and today, essentially we all just do Monte Carlo because we have the computation available for it. I drifted in and out of loving and skimming the book to get it done, but there are certainly lessons I'll draw upon. I just wish it would've bee
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Covers the history of Bayes' rule but there's little on actual mathematical application. The book was heavy on mathematician drama and light on data-driven aha! moments. If you are interested mainly in History of Mathematics, raise this review to 3.5 stars. But the book's subtitle is misleading: rather than "_how_ Bayes' rule cracked the Enigma code..." it should read "_that_ Bayes' rule cracked the Enigma code..." since there's little "how" to be had. An interesting piece of history, but ultima
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“The combination of Bayes and Markov Chain Monte Carlo has been called "arguably the most powerful mechanism ever created for processing data and knowledge."
Almost instantaneously MCMC and Gibbs sampling changed statisticians' entire method of attacking problems. In the words of Thomas Kuhn, it was a paradigm shift. MCMC solved real problems, used computer algorithms instead of theorems, and led statisticians and scientists into a worked where "exact" meant "simulated" and repetitive computer operations replaced mathematical equations. It was a quantum leap in statistics.”
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Almost instantaneously MCMC and Gibbs sampling changed statisticians' entire method of attacking problems. In the words of Thomas Kuhn, it was a paradigm shift. MCMC solved real problems, used computer algorithms instead of theorems, and led statisticians and scientists into a worked where "exact" meant "simulated" and repetitive computer operations replaced mathematical equations. It was a quantum leap in statistics.”
“Yet Laplace had built his probability theory on intuition. As far as he was concerned, "essentially, the theory of probability is nothing but good common sense reduced to mathematics. It provides an exact appreciation of what sound minds feel with a kind of instinct, frequently without being able to account for it.”
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