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This section will attempt to make some of the basic ideas accessible to someone with a limited understanding of probability theory (if you understood the last section, which reviews conditional probabilities and the calculation of basic probabilities, then you should be able to grasp the concepts in this section). The reader who seeks a deeper and more thorough treatment of Bayesian networks should consult one of a number of books that provide a more rigorous treatment of the subject.10 A Bayesian network may be envisioned as a causal diagram that represents a web of cause and effect relationships (alternatively, a Bayesian network may represent a belief network that shows the factors that in uence the degree of belief in a hypothesis). See Figure 8.11 for a simple Bayesian network example. The nodes, indicated by the rectangles, represent variables. In the gure all variables are discrete, having only two possible values, yes and no. In a more complex application, the variables can assume more than two discrete values for example, bad weather can assume values (1) extremely bad, (2) bad, (3) slightly bad, and (4) not bad or can be continuous for example, plane late, can be the number of hours a plane is late, from 0 to in nity. To simplify matters, and later on to simplify the calculation of conditional probabilities, we consider only discrete nodes having two possible values. The directed links in a Bayesian network specify the causal or in uential relationships between the variables. 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