Lecture 12

Bayesian networks are a useful tool. First, they are particularly useful for describing processes composed of locally interacting components; that is, the value of each component directly depends on the values of a relatively small number of components. Second, statistical foundations for learning Bayesian networks from observations, and computational algorithms to do so are well understood and have been used successfully in many applications. Finally, Bayesian networks provide models of causal influence: although Bayesian networks are mathematically defined strictly in terms of probabilities and conditional independence statements, a connection can be made between this characterization and the notion of direct causal influence. (Heckerman, Meek & Cooper 1997; Pearl & Verma 1991; Spirtes, Glymour & Scheines 1993).