Sharon bertch mcgrayne biography of michael

McGrayne provides a fascinating account of the modern use of this result in matters as diverse as cryptography, assurance, the investigation of the connection between smoking and cancer, RAND, the identification of the author of certain papers in The Federalist, election forecasting and the search for a missing H-bomb. The general reader will enjoy her easy style and the way in which she has successfully illustrated the use of a result of prime importance in scientific work.

Compelling, fast-moving prose. Readers will be amazed at the impact that Bayes' rule has had in diverse fields, as well as by its rejection by too many statisticians. I was brought up, statistically speaking, as what is called a frequentist But reading McGrayne's book has made me determined to try, once again, to master the intricacies of Bayesian statisics.

I am confident that other readers will feel the same. Rather, McGrayne offers a very thorough, informative, and often entertaining in our humble opinion discussion of the Bayesian perspective Top holiday reading. On pages 78 and 79, references to a circle miles across should be changed to a rectangle measuring 50 x miles, that is, to an area of 10, square miles.

Alan Chodos spotted this one. Pageline both the first y and the third y in the James-Stein formula should have a bar on top. A kind reader known to me only as Richard pointed this problem out to me. Pageline 11 from the bottom: change "32 kilobytes" to "32K words. The sharon bertch mcgrayne biography of michael does not affect the answer.

McGrayne describes actuarial, business, and military uses of the Bayesian approach, including its application to settle the disputed authorship of 12 of the Federalist Papers, and its use to connect cigarette smoking and lung cancer. All of this is accomplished through compelling, fast-moving prose. The reader cannot help but enjoy learning about some of the more gossipy episodes and outsized personalities.

It will broaden and deepen the field of reference of the more expert statistician, and the general reader will find an understandable, well-written, and fascinating account of a scientific field of great importance today. Dale, Notices of the American Mathematical Society "A very engaging book that statisticians, probabilists, and history buffs in the mathematical sciences should enjoy.

This book describes in vivid prose, accessible to the lay person, the development of Bayes' rule over more than two hundred years from an idea to its widespread acceptance in practice. This book does so much more, however, uncovering the almost secret role of Bayesian analysis in a stunning series of the most important developments of the twentieth century.

What a revelation and what a delightful read! McGrayne provides a fascinating account of the modern use of this result in matters as diverse as cryptography, assurance, the investigation of the connection between smoking and cancer, RAND, the identification of the author of certain papers in The Federalist, election forecasting and the search for a missing H-bomb.

The general reader will enjoy her easy style and the way in which she has successfully illustrated the use of a result of prime importance in scientific work. A great story. Kass, Carnegie Mellon University. I truly admire [McGrayne's] style of writing, and. She lives in Seattle. Convert currency. Add to basket. Condition: Used; Very Good. All orders are dispatched as swiftly as possible!

Buy with confidence! Greener Books. Seller Inventory Contact seller. Condition: Good. No Jacket. Spine may show signs of wear. Seller Inventory GI3N Condition: Very Good. And what's worse is how the end slowly descends into the breathless language of tech marketing: here is a list of unrelated problems, they are unsolvable by traditional methods, but guess which magical sharon bertch mcgrayne biography of michael is being used to solve them?

Ultimately, Bayesian thinking is a useful way to make decisions and a powerful statistical paradigm for solving problems with limited information. While it may be the preferred approach now in a lot of fields, it is also not a panacea, nor are frequentist methods being put to pasture. If the Bayesian wars have reached some kind of conclusion, it is that knowing when to reach for MLE or MAP, p-values or posteriors, or some combination of both paging Empirical Bayesians!

Rafael Maia. 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 it could've been, because the stories are indeed very interesting. Also, even with the glossary at the end, many technical aspects are reported without any explanation of why they were important and what they meant to Bayesian statistics, giving the feel of careless "name-throwing".

It also feels like the the author tried to cramp together every single case of applied Bayesian approaches, in a way that the important cases are often drowned in a noisy list of achievements. In some cases, after a very long build-up, it turns out that Bayes theorem either wasn't even used or wasn't important, and the story doesn't tie up with a future Bayesian achievement spanning from that.

It's just there because it remotely related to Bayes theorem and thus "had" to be included in the book. So, in my opinion, this is a book that is rich in interesting stories and information, but the reader will need extra willpower to drag through the unexciting narrative and the excessive "noise". 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. 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. My memory indicated Bodanis included Laplace in his book, but attempts to confirm this have not been successful.

Maybe credit is due another author. It is possible I have oddly attributed my knowledge of Laplace to someone who didn't even include him a book. Memory is so strange. I remember being a bit wowed and intrigued after first learning about Laplace; and yet, not doing any further research. What a shame that would have been. Laplace was an exceptional scientist.

He not only came up with Bayes Rule by himself, but he also did more work than Bayes to contribute to humankind's understanding of probability, fought vigorously to separate religion and scientific inquiry, insisted on facts over belief, and was extremely productive in developing a foundation for statistics-- despite receiving so little reward.

You will be treated to how, as a thank you from society, his life and reputation were ruined. Poor Laplace. The author also provided a fairly good biography of other contributors to Bayes' Rule development and application throughout history. The rule itself was extremely unpopular. It's successes were hidden in wartime to protect war secrets. Those who used it were often bullied by the larger statistics community.

And yet, the theory lived on, often under the radar, to continue helping researchers solve the hard problems. When the author provided a survey of how Bayes was used, I was familiar with the instances she highlighted but didn't realize Bayes was the method used to solve the problems at hand. Since the author included, what I can only imagine, was every instance in which Bayes was employed, at times I felt like, "Yes, I have got it.

Move on. Author 31 books 91 followers. 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. Dennis Boccippio. 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.

Sharon bertch mcgrayne biography of michael: 12 years ago more. singularitysummit. K.

This is even more impressive as she is a journalist, rather than a statistician. McGrayne immerses the reader in what can only be called "lush" detail of the history, from personalities to global events. The con's: This a very dense sharon bertch mcgrayne biography of michael. Not dry in an academic sense, but a lot of material to consume.

At times I had to summon extra reserves of motivation to proceed to the next chapter. The topic is also a difficult one to communicate solely through narrative - more than once I found myself wishing for just a little bit of math-by-the-way-of-example to help grasp the concepts. With such, this could actually serve well as an educational vehicle.

While already familiar with Bayes, the application in some of the historical examples was, for me, elusive. To me, this is where the real excitement lies, if "excitement" is the correct term! Overall - if statistics, scientific inference, decision theory or machine learning excite you, this is probably a book to have under your belt. Reading the history of Bayesian vs frequentist wars triggered some good musing and reflection on the critical question of "how to make inferences when too little, rather than too much, data are at hand".

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. 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 data about its spatial relation right or left to a succession of balls rolled randomly across the table.

The idea was originated by Bayes, but was refined by Pierre-Simon Laplace, a French astronomer and mathematician. Laplace wanted more: as a working scientist, he wanted to know the probability that certain measurements and numerical values associated with a phenomenon were realistic. P C Ethe probability of a hypotheses given informationequals P prior Cour initial estimate of its probability, times P E Cthe probability of each new piece of information under the hypothesisdivided by the sum of the probabilities of the data in all possible hypotheses.

The history of science is directed by many non-scientific incidents. The theorem fell out of favor in scientific circles, replaced by frequency-based probability methods. The focus of inquiry shifted to sampling methodology. As a result, we are all familiar with analysis based on the bell-shaped curve. How Bayes's Theorem stayed alive consumes the remainder of McGrayne's story.

Much of this shift was due to the violently anti-Bayesian position of Ronald Fisher, a prominent British geneticist and statistician. In the 's and '30's, the idea survived in the fields of financial economics, paternity law, biostatistics, and geology. Because it provided practical data that could be used as a basis for action. During World War II the theorem received another boost.

It was instrumental in cracking the German Enigma code. The effort was led by Alan Turing, whose interests spanned applied mathematics, machine language, codes and logic. He was a man of unconventional interests, a non-linguist, and a non-statistician. Above all, Turing was attracted to the Enigma problem because it was a challenge.

No one else wanted to tackle the problem, and he enjoyed working alone. One problem for the resuscitation of Bayes's theorem was that all of this wartime work was classified. Post-war practice was kept alive in schools of business and actuary science. Academic mathematicians and statisticians ignored it. To me, the most exciting post-war application was Albert Madansky's report on the probability of an accidental H-bomb detonation.

Since there had never been an accidental detonation, frequency theory was of no help. Madansky's calculations included the fact that between and there had been more than 16 nuclear weapon related incidents — accidental drops, plane crashes, and handling errors of unarmed weapons. Even the air force calculated that 5 major plane accidents perflying hours was a reasonable assumption.

The report resulted in significant changes in nuclear weapons handling procedures. The early chapters of the book felt slow. They are primarily of interest to the historian seeking to verify the contributions to the theory by specific mathematicians. However, McGrayne does a convincing job of stressing the importance of Bayes's Theorem without going into the mathematical details.

She also reveals interesting information about how academic rivalries, military secrecy, politics, and inter-disciplinary studies influence scientific progress. The latter is perhaps the most significant lesson this book offers to the general reader. Her many detailed historic examples make a compelling case for the importance of Bayes's Theorem in scientific analysis.

It is the third book I read about statistics in a short while and it is probably the strangest. P A Bthe posterior, is the degree of belief having accounted for B. I was never really comfortable with its applications. I was probably wrong again, given all what I learnt after reading Sharon Bertsch McGrayne's rich book. But I also understood why I was never comfortable: for three centuries, there's been a quasi-religious war between Bayesians and Frequentists on how to use probabilities.

Are these linked to big, frequent numbers only or can they be applied for rare events? What is the probability of a rare event which may never occur or maybe just once? I have more than 5' entrepreneurs, and more than 1' are serial. I have results showing that serial entrepeneurs are not on average better than one-time, using frequency and classical methods.

If you want a good summary of the book, read the review by Andrew I. Daleby pdf. McGrayne illustrates the "recent" history of statistics and probabilities through famous Laplace and less famous Bayes scientists, through famous the Enigma machine and Alan Turing and less famous lost nuclear bombs stories and it is a fascinating book.

I am not convinced it is great at explaining the science, but the story telling is great. Indeed, it may not be about science at all. It obviously reminded me of Einstein's famous quote: "God does not play dice with the universe. AJ Armstrong. 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.

Sharon bertch mcgrayne biography of michael: Sharon Bertsch McGrayne is

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 critical to enjoying the book and understanding the majority of its content or at least its context in the narrative.

The book's thematic focus is, obviously, Bayes' rule, but it touches on a whole panoply of ways in which science and mathematics have evolved into tools for everyday decision-makingincluding many of the roots of the modern world. Of particular interest is the tension between theory and practice, and how the Rule found its niche amongst those just trying to get work done, even while it was being ignored or rejected by pure theoreticians.

A worthy read for anyone interested in these areas. My only complaints are ones of emphasis, and might be more a matter of taste than anything else: I wish the author had been clearer about how what "Bayes' Rule" means has evolved and the fact that people who objected to it in the 19th century and those that objected to it in the middle part of the 20th were not necessarily objecting to the same thing.

Sharon bertch mcgrayne biography of michael: The Theory That Would Not Die

She also could have been clearer that what is "Baysian" has due to its current fashionability has broadened to the point that Bayes and LaPlace wouldn't recognize it, and that many so-called "Bayesian" techniques are strongly influenced by things that Frequentists would have had no problems with. Objecting to arbitrary priors isn't the same thing as objecting to many of the things that are now called Bayesian techniques.

Finally, I wish she had been a little clearer that Bayes' value wasn't strongly evident until the ubiquity of cheap computational resources and the familiarity with the types of problems that large, complex systems generate became widespread in the theoretical community. Both of these trends had to wait until the later part of the 20th century, and so, frankly, did widespread acceptance of probabilistic reasoning under uncertainty.

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 As with many pop math books, I wish there was more math, but I also understand why McGrayne made the choices she did to keep the book light on technical details and heavy on the historical anecdotes.

She does a wonderful job of telling a very interesting story and one that more people should know. 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 LaplaceThis type of writing would be bad enough if the importance of Bayesian analysis were clearly explained, but it isn't.

Maybe I should stop rating books. I give this a somewhat tepid 4 stars.

Sharon bertch mcgrayne biography of michael: In the first-ever account

I enjoyed parts 1, 2, and 5, but found the story to drag at times in parts 3 and 4 exceptions for the sections on smoking deaths and searching for nuclear weapons. I think that the Signal and the Noise does a better job of showing and actually explaining Bayes theorem, but it's also a much longer book and has room to do that. Really an interesting, unusual book that combines history, math, and decision theory into a discussion about several of the most useful ideas in probability theory.

Although a bit preachy at times, and clearly of the perspective that frequentists slowed down several fields of science, I think it tells the tale of the evolution of probability theory very well -- ironically the whole field, not just the Bayesian aspects of probability.