Upon engineering a Bayesian network for the early detection of Classical Swine Fever in pigs, we found that the commonly used approach of separately modelling the relevant observable variables would not suffice to arrive at satisfactory performance of the network: explicit modelling of combinations of observations was required to allow identifying and reasoning about patterns of evidence. In this paper, we outline a general approach to modelling relevant patterns of evidence in a Bayesian network. We demonstrate its application for our problem domain and show that it served to significantly improve our network’s performance.
van der Gaag, L. C., Bolt, J., Loeffen, W. L. A., & Elbers, A. R. W. (2010). Modeling patterns of evidence in Bayesian networks: a case-study in Classical Swine Fever. Lecture Notes in Computer Science, 6178, 675-684. https://doi.org/10.1007/978-3-642-14049-5_69