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The “Leaky noisy-OR” is a common method used to simplify the elicitation of complex conditional probability tables in Bayesian networks involving Boolean variables. It has proven useful for approximating the required relationship in many real-world situations where there are two or more variables that are potential causes of a single effect variable. However, one of the properties of leaky noisy-OR is Conditional Inter-causal Independence (CII). This property means that ‘explaining away‘ behaviour-one of the most powerful benefits of BN inference is not present when the effect variable is observed as false. Yet, for many real-world problems where the leaky noisy-OR has been considered, this behaviour would be expected, meaning that leaky noisy-OR is deficient as an approximation of the required relationship in such cases. There have been previous attempts to adapt noisy-OR to resolve this problem. However, they require too many additional parameters to be elicited. We describe a simple but powerful extension to leaky noisy-OR that requires only a single additional parameter. While it does not solve the CII problem in all cases, it resolves most of the explaining away deficiencies that occur in practice. The problem and solution is illustrated using an example from intelligence analysis.

The primary challenge in constructing a Bayesian Network (BN) is acquiring its Conditional Probability Tables (CPTs). CPTs can be elicited from domain experts; however, they scale exponentially in size, thus making their elicitation very time consuming and costly. Das [1] proposed a solution to this problem using the weighted sum algorithm (WSA). In this paper we present two empirical studies that evaluates the WSA's efficiency and accuracy, we also describe an extension for the algorithm to deal with one of its shortcomings. Our results show that the estimates obtained using the WSA were highly accurate and make significant reductions in elicitation.