When using Bayes' Law to perform inference, instead of A and B or X and Y, the equations often use E and H as their variables. What do E and H stand for? How does that fit in with the idea of Bayesian reasoning?
Define "prior" and "posterior" probabilities (also known as "a priori" and "a posteriori").
Write at least two different ways that the joint probability p(X,Y,Z) could be broken down into a product of three probabilities using the chain rule.
The naïve Bayes classifier is so called because it makes a series of very naïve conditional independence assumptions. What are they?
