Assessing the weighted sum algorithm for automatic generation of Probabilities in Bayesian Networks
Simon Baker, Emília Mendes · 2010
A Bayesian Network (BN) is a probabilistic reasoning technique, which to date has been used in a broad range of applications. One of the key challenges in constructing a BN is obtaining its Conditional Probability Tables (CPTs). CPTs can be learnt from data (when available), elicited from domain experts, or a combination of both. Eliciting from domain experts provides more flexibility; however, CPTs grow in size of exponentially, thus making their elicitation process very time consuming and costly. Previous work proposed a solution to this problem using the weighted sum algorithm (WSA) [9]; however no empirical results were given on the algorithm's elicitation reduction and prediction accuracy. Hence the aim of this paper is to present two empirical studies that assess the WSA's efficiency and prediction accuracy. Our results show that the estimates obtained using the WSA were highly accurate and make significant reductions in elicitation.