I’m not sure when I first heard of Bayes’ theorem. But I only really started paying attention to it over the last decade, after a few of my wonkier students touted it as an almost magical guide for navigating through life.
My students’ rants confused me, as did explanations of the theorem on Wikipedia and elsewhere, which I found either too dumbed-down or too complicated. I conveniently decided that Bayes was a passing fad, not worth deeper investigation. But now Bayes fever has become too pervasive to ignore.
Bayesian statistics “are rippling through everything from physics to cancer research, ecology to psychology,” The New York Times reports. Physicists have proposed Bayesian interpretations of quantum mechanics and Bayesian defenses of string and multiverse theories. Philosophers assert that science as a whole can be viewed as a Bayesian process, and that Bayes can distinguish science from pseudoscience more precisely than falsification, the method popularized by Karl Popper.
Artificial-intelligence researchers, including the designers of Google’s self-driving cars, employ Bayesian software to help machines recognize patterns and make decisions. Bayesian programs, according to Sharon Bertsch McGrayne, author of a popular history of Bayes’ theorem, “sort spam from e-mail, assess medical and homeland security risks and decode DNA, among other things.” On the website Edge.org, physicist John Mather frets that Bayesian machines might be so intelligent that they make humans “obsolete.”
To read more, click here.