A moment later, you notice him pull into the hospital and your anger melts away. Or, maybe someone close is dying. An obscure rule from Probability Theory, called Bayes Theorem, explains this very well. This 9, word blog post is a complete introduction to Bayes Theorem and how to put it to practice. In short, Bayes Theorem is a framework for critical thinking. Thinking the driver is an asshole is normal.

Bayes Theorem is the handiwork of an 18th-century minister and statistician named Thomas Bayes, first released in a paper Bayes wrote entitled "An Essay Towards Solving a Problem in the Doctrine of Chances. There is some debate among economists on whether credit should also be given to another economist, Richard Price, who edited and corrected Bayes' paper in , after his death in Another wrinkle on Bayes Theorem stems from a paper by French mathematician Pierre-Simon Laplace, who was apparently unaware of Bayes original thesis. Laplace formalized the Bayes concept and is now viewed by economists as the individual who should share the credit for developing what's known as the "Bayesian probability. Alan Turing, a British mathematician, used Bayes Theorem to assess the translations culled from the Enigma encryption machine used to crack the German messaging code. Applying probability models, Turing and his staff were able to break down the almost infinite number of possible translations based on the messages that were most likely to be translatable, and ultimately crack the German Enigma code. Interestingly, there is no known portrait of Bayes in existence, and nobody really knows what he looked like there is a sketched image floating around the internet, but it's never been officially confirmed as the "real Bayes.

Yesterday, an interesting conundrum came to me. Imagine that one came back negative and the other came back positive… which can and does happen. Here is the tricky question… which one do you trust? The positive or the negative? Mathematicians have an entire theory for dealing with problems exactly like this one.

In probability theory and statistics , Bayes' theorem alternatively Bayes' law or Bayes' rule ; recently Bayes—Price theorem [1] : 44, 45, 46 and 67 , named after the Reverend Thomas Bayes , describes the probability of an event , based on prior knowledge of conditions that might be related to the event. One of the many applications of Bayes' theorem is Bayesian inference , a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence.

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