I'm in the middle of designing and building a system which uses Bayesian models.

One thing that struck me early is that while Bayes itself is rock solid, like arithmetic, when you go to apply it the results live or die on the quality of the models, and the relevance/realism of the evidence used to train them. GIGO.

But once you do have a good, relevant, signal-producing model, then, using it is a bit like doing a multi-dimensional lookup, or function call. Conceptually easy to understand, and, in many cases (depending, of course, on the details) cache-friendly.