EM Algorithm for Symmetric Causal Independence Models

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function. The developed algorithm enables us to assess the practical usefulness of the symmetric causal independence models, which has not been done previously. We evaluate the classification performance of the symmetric causal independence models learned with the presented EM algorithm. The results show the competitive performance of these models in comparison to noisy OR and noisy AND models as well as other state-of-the-art classifiers.