Discovery of Pseudo-Independent Models from Data

Graphical models such as Bayesian networks (BNs) and decomposable Markov networks (DMNs) have been widely applied to probabilistic reasoning in intelligent systems. Figure 1 illustrates a BN and a DMN on a trivial uncertain domain: Virus can damage computer files and so can a power glitch. Power glitch also causes VCR to reset. The BN in (a) has four nodes, corresponding to four binary variables (taking values from {true, false}. The graph structure encodes a set of dependence and independence assumptions, e.g., that f is directly dependent on v and p but is independent of r once the value of p is known. Each node is associated with a conditional probability distribution conditioned on its parent nodes, e.g., P (f |v, p). The joint probability distribution is the product P (v, p, f, r) = P (f |v, p)P (r|p)P (v)P (p). The DMN in (b) has two groups of nodes that are maximally