Eliciting and analyzing expert judgment: a practical guide
Eliciting and analyzing expert judgment: a practical guide
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
Monotonicity in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bringing order into bayesian-network construction
Proceedings of the 3rd international conference on Knowledge capture
How to elicit many probabilities
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Attaining monotonicity for Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
When learning naive bayesian classifiers preserves monotonicity
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
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In many realistic problem domains, the main variable of interest behaves monotonically in the observable variables, in the sense that higher values for the variable of interest become more likely with higher-ordered observations. This type of knowledge appears to naturally emerge from experts during knowledge elicitation, without explicit prompting from the knowledge engineer. The experts' concept of monotonicity, however, may not correspond to the mathematical concept of monotonicity in Bayesian networks. We present a method that provides both for verifying whether or not a network exhibits the properties of monotonicity suggested by the experts and for studying the violated properties with the experts. We illustrate the application of our method for a real Bayesian network in veterinary science.