It’s been more than 250 years since the appearance of Bayes theorem (named after English statistician, philosopher and Presbyterian minister Thomas Bayes: 1701-1761), one of the two fundamental inferential principles of mathematical statistics. Two contending philosophical parties, the Bayesians and the frequentists, have been vying for supremacy over the past two-and-half centuries, but it may be the big data explosion of late that provides the justification for a Bayesian/frequentist alliance designed to deal with the enormous and complicated data sets modern technology is producing.
If you’re a data scientist in-training, it may be a good time to sit back and consider the rich history of the field of statistics and review where we’ve been and where we’re going. I found an excellent survey article courtesy of the American Mathematical Society (of which I am a proud member): “A 250-Year Argument: Belief, Behavior, and the Bootstrap” that is good way to get up to speed. I believe all data scientists should possess a firm foundation in mathematical statistics and probability theory — understanding Bayes theorem is a big part of this formula. Read and enjoy!
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