Related Work

Before we consider new applications of dependency networks, we review related work on the basic concepts. As we have already mentioned, several researchers who developed Markov networks began with an examination of what we call consistent dependency networks. For an excellent discussion of this development as well as original contributions in this area, see Besag (1974). Besag (1975) also described an approach called pseudo-likelihood estimation, in which the conditionals are learned directly--as in our approach--without respecting the consistency constraints. We use the name pseudo-Gibbs sampling to make a connection to his work. Tresp and Hofmann (1998) describe (general) dependency networks, calling them Markov blanket networks. They stated and proved Theorem 3, and evaluated the predictive accuracy of the representation on several data sets using local distributions consisting of conditional Parzen windows.