Causal independence for probability assessment and inference using Bayesian networks (1994)
| Venue: | IEEE Trans. on Systems, Man and Cybernetics |
| Citations: | 53 - 2 self |
BibTeX
@TECHREPORT{Heckerman94causalindependence,
author = {David Heckerman and John S. Breese},
title = {Causal independence for probability assessment and inference using Bayesian networks},
institution = {IEEE Trans. on Systems, Man and Cybernetics},
year = {1994}
}
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Abstract
ABayesian network is a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems. When there are many potential causes of a given e ect, however, both probability assessment and inference using a Bayesian network can be di cult. In this paper, we describe causal independence, a collection of conditional independence assertions and functional relationships that are often appropriate to apply to the representation of the uncertain interactions between causes and e ect. We show how the use of causal independence in a Bayesian network can greatly simplify probability assessment aswell as probabilistic inference. 1
