Bayesian networks are a very general tool that can be used for a large number of problems reasoning (using the Bayesian inference algorithm), learning (using the expectation maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks).

Bayesian model comparison is a method of model selection based on Bayes factors.

Bayesian updating is particularly important in the dynamic analysis of a sequence of data.

And the point about Bayesian decision theory is it gives you the mathematics of the optimal way to combine your prior knowledge with your sensory evidence to generate new beliefs.

As we go around, we learn about statistics of the world and lay that down, but we also learn about how noisy our own sensory apparatus is, and then combine those in a real Bayesian way.