: We construct the belief function that quantifies the agent' beliefs about which event of will occurred when he knows that the event is selected by a chance set-up and that ...
In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop...
Ross D. Shachter, Stig K. Andersen, Peter Szolovit...
A logic is de ned that allows to express information about statistical probabilities and about degrees of belief in speci c propositions. By interpreting the twotypes of probabili...
Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intell...
Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction whe...
We show how to nd a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the rst step in the method of conditioning for inference....
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach--a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabil...
Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, ...