Generalized Non-impeding Noisy-AND Trees

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. Its complexity is generally exponential in 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 a linear complexity, in terms of both the number of parameters and the size of the tree topology. As originally proposed, the model allows only binary effect and cause variables. This work generalizes the model to multivalued effect and causes, and analyzes key properties.