Causal independence for probability assessment and inference using Bayesian networks
- 1 November 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Vol. 26 (6) , 826-831
- https://doi.org/10.1109/3468.541341
Abstract
A Bayesian 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 effect, however, both probability assessment and inference using a Bayesian network can be difficult. 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 effect. We show how the use of causal independence in a Bayesian network can greatly simplify probability assessment as well as probabilistic inference.Keywords
This publication has 21 references indexed in Scilit:
- Causal independence for probability assessment and inference using Bayesian networksIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1996
- Learning Bayesian networks: The combination of knowledge and statistical dataMachine Learning, 1995
- Decision-theoretic troubleshootingCommunications of the ACM, 1995
- Real-world applications of Bayesian networksCommunications of the ACM, 1995
- Causal diagrams for empirical researchBiometrika, 1995
- A Bayesian method for the induction of probabilistic networks from dataMachine Learning, 1992
- Connectionist learning of belief networksArtificial Intelligence, 1992
- Sequential updating of conditional probabilities on directed graphical structuresNetworks, 1990
- Statistics and Causal InferenceJournal of the American Statistical Association, 1986
- Bayesian Inference for Causal Effects: The Role of RandomizationThe Annals of Statistics, 1978