On Compatible Priors for Bayesian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Network Refinement Via Machine Learning Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaining Confidence in Software Inspection Using a Bayesian Belief Model
Software Quality Control
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Expert Knowledge and Its Role in Learning Bayesian Networks in Medicine: An Appraisal
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland
Environmental Modelling & Software
Credible classification for environmental problems
Environmental Modelling & Software
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Model criticism of Bayesian networks with latent variables
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Using new data to refine a Bayesian network
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Tradeoffs in constructing and evaluating temporal influence diagrams
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Gaining confidence in the software development process using expert systems
SAFECOMP'06 Proceedings of the 25th international conference on Computer Safety, Reliability, and Security
Reversibility and equivalence in directed markov fields
Mathematical and Computer Modelling: An International Journal
Inferencing the graphs of causal Markov fields
Mathematical and Computer Modelling: An International Journal
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Probabilistic expert systems based on Bayesian networks require initial specification of both qualitative graphical structure and quantitative conditional probability assessments. As (possibly incomplete) data accumulate on real cases, the parameters of the system may adapt, but it is also essential that the initial specifications be monitored with respect to their predictive performance. A range of monitors based on standardized scoring rules that are designed to detect both qualitative and quantitative departures from the specified model is presented. A simulation study demonstrates the efficacy of these monitors at uncovering such departures.