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Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning
Abstract
The paper reports recent results from the theory of Bayesian networks, which offer a viable formalizsm for realizing the computational objectives of connectionist models of knowledge. In particular, we show that the Bayseian network formalism is supportive of self-activated, multidirectional proagation of evidence that converges rapidly to a globally-consistent equilibrium.
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