Verification and validation of Bayesian knowledge-bases
Data & Knowledge Engineering
Independence Semantics for BKBs
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Combining knowledge from different sources in causal probabilistic models
The Journal of Machine Learning Research
Reasoning with BKBs – Algorithms and Complexity
Annals of Mathematics and Artificial Intelligence
On automatic knowledge validation for Bayesian knowledge bases
Data & Knowledge Engineering
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
On a framework for the prediction and explanation of changing opinions
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Constructing situation specific belief networks
UAI'98 Proceedings of the Fourteenth 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
Hidden Source Behavior Change Tracking and Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
DemocraticOP: A Democratic way of aggregating Bayesian network parameters
International Journal of Approximate Reasoning
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We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A). They may even disagree on the entire structure of dependencies among a set of variables in a probabilistic network. In our proposed solution to this problem, we represent the probabilistic models as Bayesian Knowledge Bases (BKBs) and propose an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert decision making by providing a framework in which all opinions can be preserved and reasoned over.