a book about how bayes’ rule cracked the enigma code & other exploits

bayesian theory applicationsthe theory that would not die (2011) by sharon bertsch mcgrayne is about the 200 year history of bayes rule which was marked by obscurity, secrecy, derision & finally glory but not before dividing the statistics community for many years <bayesians vs. frequentists | the fissure likely still exists & will continue to exist, it’s a philosophical almost religious difference> / computational resources now being both magnitudes more powerful & cheap, bayesian statistics is considered by some as the analytic tool of the 21st century

this is the most fascinating & engaging book i have read so far this year! / not only because we use bayes theory in our work but also discovering its breadth of applications such as:  cracking the enigma code which helped the allies win the war | searching for the debris of air france flight 447 & navy searches <submarines, downed weapon-carrying b52s> | setting compensation insurance | more accurate probabilities of rare events which led to increased safety measures <warned but not heeded:  three mile island, challenger space shuttle> | predicting election results, the first being kennedy vs. nixon <until the media resorted to exit polls | though nate silver famously predicted correctly state outcomes in 2008 using hierarchical bayes> | identifying the linkage between lung cancer & cigarette smoking | identifying the risk factors for cardiovascular disease <both with far reaching public health implications> | powering the google search engine | microsoft windows os & spam filtering | helping stanley the driverless car win the darpa challenge in 2005 <as well as driving google’s driverless car> | recommendation engines for movies, books, etc. | uncovering the authorship of 12 federalist papers

importantly too, many analytic techniques were developed to facilitate the application of bayes theory, among them: asymptotic approximations \ sequential analysis / gibbs sampling \  markov chain monte carlo <MCMC> simulation \ hidden markov models / kalman filter <though kalman denies that bayes rule had anything to do with his invention, it was proven mathematically by aoki that it can be derived from bayes rule>

at the very basic level, bayes rule is intuitive | one might even say, this is how we naturally think & make decisions: “initial beliefs + recent objective data = a new & improved belief” / after reading the drama surrounding bayes theory, it is alluring to declare sole allegiance to bayes \ i resist that because a good analyst will use the tool that is most appropriate to the problem at hand / knowing how to apply both bayesian & classical statistical techniques is the best position to take \ i highly recommend this book to anyone involved in data analysis | and to those who are not, enjoy the awesomeness of probability & statistics!

i learned about this book in the ny times

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related current events:  bayes theory will not work in the case of the missing malaysian plane because the prior is incorrect <an oil slick that was incorrectly thought to have been caused by the plane>

http://america.aljazeera.com/articles/2014/3/12/mathematical-equationcouldhelpfindmissingmalaysianplane.html

 

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