Nobody knows what the mathematician Rev. Thomas Bayes looked like, but this is the picture everyone uses. The equation is Bayes' theorem.

Nate Silver, baseball statistician turned political analyst, gained a lot of attention during the 2012 United States elections when he successfully predicted the outcome of the presidential vote in all 50 states. The reason for his success was a statistical method called Bayesian inference, a powerful technique that builds on prior knowledge to estimate the probability of a given event happening.

Bayesian inference grew out of Bayes' theorem, a mathematical result from English clergyman Thomas Bayes, published two years after his death in 1761. In honor of the 250th anniversary of this publication, Bradley Efron examined the question of why Bayes' theorem is not more widely used—and why its use remains controversial among many scientists and statisticians. As he pointed out, the problem lies with blind use of the theorem, in cases where prior knowledge is unavailable or unreliable.

As is often the case, the theorem ascribed to Bayes predates him, and Bayesian inference is more general than what the good reverend worked out in his spare time. However, Bayes' posthumous paper was an important step in the development of probability theory, and so we'll stick with using his name.

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George E.P. Box, a statistician known for his body of work in time series analysis and Bayesian inference (and his quotes), recounts how he became a statistician while trying to solve actual problems. He was a 19-year-old college student studying chemistry. Instead of finishing, he joined the army, fed up with what the British government was doing to stop Hitler.

Before I could actually do any of that I was moved to a highly secret experimental station in the south of England. At the time they were bombing London every night and our job was to help to find out what to do if, one night, they used poisonous gas.

Some of England's best scientists were there. There were a lot of experiments with small animals, I was a lab assistant making biochemical determinations, my boss was a professor of physiology dressed up as a colonel, and I was dressed up as a staff sergeant.

The results I was getting were very variable and I told my colonel that what we really needed was a statistician.

He said "we can't get one, what do you know about it?" I said "Nothing, I once tried to read a book about it by someone called R. A. Fisher but I didn't understand it". He said "You've read the book so you better do it", so I said, "Yes sir".

Box eventually worked with Fischer, studied under E. S. Pearson in college after his discharge from the army, and started the Statistical Techniques Research Group at Princeton on the insistence of one John Tukey.

alphadogg writes "Judea Pearl, a longtime UCLA professor whose work on artificial intelligence laid the foundation for such inventions as the iPhone's Siri speech recognition technology and Google's driverless cars, has been named the 2011 ACM Turing Award winner. The annual Association for Computing Machinery A.M. Turing Award, sometimes called the 'Nobel Prize in Computing,' recognizes Pearl for his advances in probabilistic and causal reasoning. His work has enabled creation of thinking machines that can cope with uncertainty, making decisions even when answers aren't black or white."

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