Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

One problem faced in knowledge engineering for Bayesian networks is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most common practical solution is application of the noisy-OR (or their generalization, the noisy-MAX) gates, which take advantage of independence of causal interactions and provide a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, we propose an algorithm that fits a noisy-MAX distribution to an existing CPT and we apply it to search for noisy-MAX gates in three existing practical Bayesian networks. We show that noisy-MAX gate provides a surprisingly good fit for as many as 50% of CPTs in these networks. The importance of this finding is that it provides an empirical justification for the use of the noisy-MAX gate as a powerful knowledge engineering tool.

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Adam Zagorecki, Marek J. Druzdzel
• Keywords: Artificial Intelligence, Bayesian Networks, ECAI 2006, Knowledge Engineering, Noisy-max Gate