In his book, Neural Networks - A Comprehensive Foundation, Simon Haykin has an entire section (3.10) dedicated to how perceptrons and Bayesian classifiers are closely related when operating in a Gaussian environment. However, it is not until the end of the section that Haykin mentions that the relation is only limited to linearity. What is interesting about this is that a Perceptron can produce the same classification “model” as a Bayesian classifier, provided that the underlying data is drawn from a Gaussian distribution.
Allen Downey makes a very good case for learning advanced mathematics through programming (Check the first section of the preface of Think Bayes, titled “My theory, which is mine”). But before the reader can hit paydirt with using the Bayes theorem in programming, Downey makes you go through some elementary problems in probability, which have to be solved by hand first, if you expect to have a clear enough understanding of the concept.