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. ( )

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. ( )

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. ( )

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. ( )

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. ( )