: Jeffrey’s rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models b...
In this paper some initialwork towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reas...
In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL ...
Based Abstraction and Categorization Eric J. Horvitz∗ and Adrian C. Klein Palo Alto Laboratory Rockwell International Science Center 444 High Street Palo Alto, CA 94301 We take ...
In this paper the classical propositional assumption-based model is extended to incorporate probabilities for the assumptions. Then the whole model is placed into the framework of...
We compare the diagnostic accuracy of three diagnostic inference models: the simple Bayes model, the multimembership Bayes model, which is isomorphic to the parallel combination f...
I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief-ne...
We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that wo...