Bayesian Networks without Tears

50 AI MAGAZINE u n d e r s t a n d i n g (Charniak and Goldman 1989a, 1989b; Goldman 1990), vision (Levitt, Mullin, and Binford 1989), heuristic search (Hansson and Mayer 1989), and so on. It is probably fair to say that Bayesian networks are to a large segment of the AI-uncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsoOver the last few years, a method of reasoning using probabilities, variously called belief networks, Bayesian networks, knowledge maps, probabilistic causal networks, and so on, has become popular within the AI probability and uncertainty community. This method is best summarized in Judea Pearl’s (1988) book, but the ideas are a product of many hands. I adopted Pearl’s name, Bayesian networks, on the grounds that the name is completely neutral about the status of the networks (do they really represent beliefs, causality, or what?). Bayesian networks have been applied to problems in medical diagnosis (Heckerman 1990; Spiegelhalter, Franklin, and Bull 1989), map learning (Dean 1990), language Bayesian Networks without Tears