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This paper focuses on approaches that address the intractability of knowledge acquisition of conditional probability tables in causal or Bayesian belief networks. We state a rule that we term the "recursive noisy OR" (RNOR) which allows combinations of dependent causes to be entered and later used for estimating the probability of an effect. In the… (More)

This paper provides <i>a priori</i> cirteria for determing when a causal model is sufficiently complete to be considered a Bayesian Network, and a new representation for Bayesian Networks shown to be more computationally efficient in a wide range of circumstances than current representations.Expert Systems for domains in which uncertainty plays a major role… (More)

The issue of confidence factors in Knowledge Based Systems has become increasingly important and Dempster-Shafer (DS) theory has become increasingly popular as a basis for these factors. This paper discusses the need for an empirical interpretation of any theory of confidence factors applied to Knowledge Based Systems and describes an empirical… (More)

Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of hierarchical and/or parallel processes, rather than models of distributions (partially) known to a model builder through some… (More)

This is the Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, which was held in Seattle, WA, July 10-12, 1987

This is the Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence, which was held in Minneapolis, MN, July 10-12, 1988