Bayesian Networks

4

Theory and Algorithms for Bayesian Networks

In this chapter we will provide the theoretical foundations underpinning the

classes of BNs we explored in Chapter 1 (discrete BNs), Chapter 2 (GBNs)

and Chapter 3 (hybrid BNs). In particular, we will introduce the formal defi-

nition of a BN and its fundamental properties. We will then show how these

properties are at the base of BN learning and inference.

4.1 Conditional Independence and Graphical Separation

BNs are a class of graphical models, which allow an intuitive representation of

the probabilistic structure of multivariate data using graphs. We have intro-

duced them in Chapter 1 as the combination ...