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Published in:
2009 | OriginalPaper | Chapter
Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition
Authors : Yang Xiang, Zoe Jingyu Zhu, Yu Li
Published in: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its
n
causes, needs to be assessed for each node. It generally has the complexity exponential on
n
. The non-impeding noisy-AND (NIN-AND) tree is a recently developed causal model that reduces the complexity to linear, while modeling both reinforcing and undermining interactions among causes. Acquisition of an NIN-AND tree model involves elicitation of a linear number of probability parameters and a tree structure. Instead of asking the human expert to describe the structure from scratch, in this work, we develop a two-step menu selection technique that aids structure acquisition.