In this chapter, the ‘black box’ of the computational architecture of Bayesian networks is examined and it is shown that the architecture works according to the principles of the logic of uncertainty that we have outlined in Chapter 1. Formal proofs are not given of the mathematical results exploited because, for readers interested in practical applications of Bayesian networks and who are going to use commercial software already available, it is more important to understand why the answers provided bythe ‘black box’ can be trusted, rather than to know in any detail the mathematics of the inference engine.

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