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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.