Using the noisy-OR model can be harmful ... but it often is not

Authors:
Steven P. D. Woudenberg;Linda C. Van Der Gaag
Affiliations:
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Venue:
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Year:
2011

Citing 3
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction

IEEE Transactions on Knowledge and Data Engineering
Probabilities for a probabilistic network: a case study in oesophageal cancer

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The noisy-OR model and its generalizations are frequently used for alleviating the burden of probability elicitation upon building Bayesian networks with the help of domain experts. The results from empirical studies consistently suggest that, when compared with a fully expert-quantified network, using the noisy-OR model will just have a minor effect on the performance of a network. In this paper, we address this apparent robustness and investigate its origin. Our results show that ill-considered use of the noisy-OR model can substantially decrease a network's performance, yet also that the model has broader applicability than it was originally designed for.