Abstract
To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. It generally has the complexity exponential on n. Noisy-OR reduces the complexity to linear, but can only represent reinforcing causal interactions. The non-impeding noisy-AND (NIN-AND) tree is the first causal model that explicitly expresses reinforcement, undermining, and their mixture. It has linear complexity, but requires elicitation of a tree topology for types of causal interactions. We study their topology space and develop two novel techniques for more effective elicitation.
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Xiang, Y., Li, Y., Zhu, Z.J. (2009). Towards Effective Elicitation of NIN-AND Tree Causal Models. In: Godo, L., Pugliese, A. (eds) Scalable Uncertainty Management. SUM 2009. Lecture Notes in Computer Science(), vol 5785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04388-8_22
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DOI: https://doi.org/10.1007/978-3-642-04388-8_22
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