P(H/D) = [P(D/H) x P(H)] ÷ P(D). That is one common formulation of Bayes' theorem, and at the heart of the view of statistics associated with a mid-18th century Englishman, Thomas Bayes. I blogged about this in January. If you don't understand the notation above, and would like a primer, go here. I bring it up again because Bayesianism has attracted new attention in the blogosphere. Sometimes the above is called "Bayes' rule," because it suggests a rule for constantly updating your views of the likelihood of events. Your hypothesis of probability of H on Tuesday (the prior ) is to be updated according to what happens or doesn't happen Wednesday, thereby yielding the posterior probability. Econoblogger Noah Smith wrote a post he calls " Bayesian Superman." He combines a (male)...