Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Dynamic Bayesian Belief Networks Using Conditional Phase-Type Distributions
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
International Journal of Approximate Reasoning
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Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effect does not depend on what other causes have previously been applied. In this paper, we introduce art equivalent a temporal characterization of causal independence based on a functional representation of the relationship between causes and the effect. In this representation, the interaction between causes and effect can be written as a nested decomposition of functions. Causal independence can be exploited by representing this decomposition in the belief network, resulting in representations that are more efficient for inference than general causal models. We present empirical results showing the benefits of a causal-independence representation for belief-network inference.