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Loading... The Theory that Would Not Die: How Bayes' Rule Cracked the Enigma Code,…by Sharon Bertsch McGrayneNone. Interesting subject, very poorly written (  ) gsatell | Oct 25, 2012 |  A well written history of Bayes Theorem. I found the information fascinating and educational. This is one of the best books I have read in years. I strongly recommend this to anyone who has an interest in statistics, science, and history. (  ) GlennBell | Sep 20, 2012 | An enjoyable collection of biographies of Bayesians and their 'enemies'. Not much was made of the irony that neither Thomas Bayes nor Pierre Laplace would have been 'Bayesians'. The most 'practical' part of the book was the indirect pointer to the OpenBUGS software project, http://www.openbugs.info/w/. (I'd like to explore using it for making software project estimates ... even if it's already been done.) For me, one of the most interesting factoids was that Bayes came up with his approach as a response to David Hume's thoughts on the problem of induction; Bayes wanted to show that it was probable that God was the creator of the universe. Towards the end of the book, McGrayne mentions that some modern-day philosophers have carried on in that vein. All in all, it was good fun. (  ) Embarquer | Apr 28, 2012 | The book basically says that some people like using Bayesian statistics, and other people don't think that Bayesian statistics should be utilized. However, it did not discuss in any detail how to apply Bayesian statistics to any actual problems, nor does it give any numeric examples. Although there were many pages of references in the back, the book did not seem to make any points clearly. (  ) FlyingMonster | Mar 22, 2012 | I love Bayesian probability, and here's a book about it! Actually it's mostly about the people and the politics surrounding the Bayesians' fight against frequentists (who didn't believe in using the subjective probabilities that full-on Bayes/Laplace analysis requires). McGrayne tracks the various uses of Bayes to solve problems across multiple fields, from cryptography to finding lost submarines, but I really wished it had been mathier: I felt like a lot of times I was taking her word that Bayes made the problem at issue easier to solve than frequentism. Concededly, it can be super hard to explain this--I have had hour-long discussions with very smart people over the Monty Hall problem. But I wished she'd tried more; her explanation of Monte Carlo modeling was clear and easy to follow. (  )1  rivkat | Feb 7, 2012 |
 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. . . .
References to this work on external resources.
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