Quantifying judgmental uncertainty: Methodology, experiences, and insights
IEEE Transactions on Systems, Man and Cybernetics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Sensitivity analysis: an aid for belief-network quantification
The Knowledge Engineering Review
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
How to elicit many probabilities
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Manifesto for Agent Technology: Towards Next Generation Computing
Autonomous Agents and Multi-Agent Systems
How experts reason: the acquisition of experts' knowledge structures
The Knowledge Engineering Review
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
Bayesian networks for social modeling
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
A proposed validation framework for expert elicited Bayesian Networks
Expert Systems with Applications: An International Journal
Decision support system for Warfarin therapy management using Bayesian networks
Decision Support Systems
Modeling crime scenarios in a Bayesian network
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Upon assessing probabilities for Bayesian belief networks, the knowledge and practical experience of experts is often the only available source of probabilistic information. It is important to realise that issues concerning human capabilities with respect to making judgements come into play when relying on experts for probability elicitation. A number of methods for the elicitation of probabilities are known from the field of decision analysis. These methods try, to some extent, to deal with those issues. I present here an overview of the issues to consider when relying on expert judgements and describe the methods that are available for expert elicitation, along with their benefits and drawbacks.