The Paper FeedA feed of Bayesian network related papers, articles, books and research that we happen across and find of interest An extension to the noisyOR function to resolve the â€˜explaining awayâ€™ deficiency for practical Bayesian network problems2019
The â€œLeaky noisyORâ€ 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 realworld situations where there are two or more variables that are potential causes of a single effect variable. However, one of the properties of leaky noisyOR is Conditional Intercausal Independence (CII). This property means that â€˜explaining awayâ€˜ behaviourone of the most powerful benefits of BN inference is not present when the effect variable is observed as false. Yet, for many realworld problems where the leaky noisyOR has been considered, this behaviour would be expected, meaning that leaky noisyOR is deficient as an approximation of the required relationship in such cases. There have been previous attempts to adapt noisyOR to resolve this problem. However, they require too many additional parameters to be elicited. We describe a simple but powerful extension to leaky noisyOR 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.
Evaluating the Weighted Sum Algorithm for Estimating Conditional Probabilities in Bayesian Networks2010
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.
