Abstract

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-network representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications. 1

Citations

5662	Probabilistic reasoning in intelligent systems: networks of plausible inference - Pearl - 1988
1080	Local computations with probabilities on graphical structures and their application to expert systems (with discussion - Lauritzen, Spiegelhalter - 1988
316	Gaussian influence diagrams - Shachter, Kenley - 1989
86	Some practical issues in constructing belief networks - Henrion - 1987
68	A computational model for causal and diagnostic reasoning in inference systems - Kim - 1983
37	A constraint�propagation approach to probabilistic reasoning - Pearl - 1986
10	A causal calculus (I). British Journal of Philosophy of Science, 11:305– 318 - Good - 1961
4	Models for Diagnosis and Detection of Combinations of Diseases - Habbema - 1976
1	Generalizing the noisy OR model to n-ary variables. Also in this proceedings - Srinivas - 1992