Bayes' Theorem
In the first part of this post, we talked about the motivations behind the epistemic interpretation of probability. Now, let’s take a look at one of the core mathematical theorems employed by those who subscribe to such an interpretation: Bayes’ Theorem (which is mentioned by Fitleson in Ep. 31). Before introducing Bayes’ Theorem, it is important to get clear on one last concept: conditional probability. The basic idea behind conditional probabilities is that we offer the probability that some event occurs, given that something else is true. It’s helpful to think about this once again in a gambling analogy. Let’s say that we want to bet on whether the Philadelphia Phillies will win the World Series next year. I might only wish to make a bet on this if one of their star players (say, Ryan Howard) is not injured this year. So we might make bets conditional on Ryan Howard not becoming injured—if Howard is injured, the bet is simply off. Referencing another of our blog posts, we say that conditional probabilities restrict the set of possible worlds we are considering (say, those possible worlds in which Ryan Howard is not injured for the 2013 season), and compute the probabilities from this restricted set of possible worlds. ...