Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decision analysis and expert systems
AI Magazine
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Use of Elvira's explanation facility for debugging probabilistic expert systems
Knowledge-Based Systems
Average and Majority Gates: Combining Information by Means of Bayesian Networks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Incorporating prior model into Gaussian processes regression for WEDM process modeling
Expert Systems with Applications: An International Journal
Fusing multiple Bayesian knowledge sources
International Journal of Approximate Reasoning
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
Iterative bayesian network implementation by using annotated association rules
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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Building probabilistic and decision-theoretic models requires a considerable knowledge engineering effort in which the most daunting task is obtaining the numerical parameters. Authors of Bayesian networks usually combine various sources of information, such as textbooks, statistical reports, databases, and expert judgement. In this paper, we demonstrate the risks of such a combination, even when this knowledge encompasses such seemingly population-independent characteristics as sensitivity and specificity of medical symptoms. We show that the criteria ``do not combine knowledge from different sources'' or ``use only data from the setting in which the model will be used'' are neither necessary nor sufficient to guarantee the correctness of the model. Instead, we offer graphical criteria for determining when knowledge from different sources can be safely combined into the general population model. We also offer a method for building subpopulation models. The analysis performed in this paper and the criteria we propose may be useful in such fields as knowledge engineering, epidemiology, machine learning, and statistical meta-analysis.