Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Probability elicitation for belief networks: issues to consider
The Knowledge Engineering Review
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Elicitator: An expert elicitation tool for regression in ecology
Environmental Modelling & Software
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling
Computers & Mathematics with Applications
Bayesian network modeling of Port State Control inspection findings and ship accident involvement
Expert Systems with Applications: An International Journal
Applying a validation framework to a working airport terminal model
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The popularity of Bayesian Network modelling of complex domains using expert elicitation has raised questions of how one might validate such a model given that no objective dataset exists for the model. Past attempts at delineating a set of tests for establishing confidence in an entirely expert-elicited model have focused on single types of validity stemming from individual sources of uncertainty within the model. This paper seeks to extend the frameworks proposed by earlier researchers by drawing upon other disciplines where measuring latent variables is also an issue. We demonstrate that even in cases where no data exist at all there is a broad range of validity tests that can be used to establish confidence in the validity of a Bayesian Belief Network.