“Bayes’ theorem is useful because what we usually know is the probability of the effects given the causes, but what we want to know is the probability of the causes given the effects. For example, we know what percentage of flu patients have a fever, but what we really want to know is how likely a patient with a fever is to have the flu. Bayes’ theorem lets us go from one to the other. Its significance extends far beyond that, however. For Bayesians, this innocent-looking formula is the F = ma of machine learning, the foundation from which a vast number of results and applications flow. And whatever the Master Algorithm is, it must be “just” a computational implementation of Bayes’ theorem. I put just in quotes because implementing Bayes’ theorem on a computer turns out to be fiendishly hard for all but the simplest problems, for reasons that we’re about to see.”