Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An entropy-based learning algorithm of Bayesian conditional trees
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Exploring the applications of user-expertise assessment for intelligent interfaces
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
Learning simple causal structures
Knowledge acquisition as modeling
Reasoning and Learning in Probabilistic and Possibilistic Networks: An Overview
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
A New Uncertainty Measure for Belief Networks with Applications to Optimal Evidential Inferencing
IEEE Transactions on Knowledge and Data Engineering
Discovering User Behavior Patterns in Personalized Interface Agents
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
An Adaptive User Interface Based On Personalized Learning
IEEE Intelligent Systems
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
International Journal of Artificial Intelligence in Education
A novel network model for molecular prognosis
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Artificial Intelligence in Medicine
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This paper describes an algorithmic means for inducing implication networks from empirical data samples. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probablistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. In order to examine the effectiveness and validity of the induction method, several Monte-Carlo simulations were conducted, where theoretical Bayesian networks were used to generate empirical data samples驴some of which were used to induce implication relations, whereas others were used to verify the results of evidential reasoning with the induced networks. The values in the implication networks were predicted by applying a modified version of the Dempster-Shafer belief updating scheme. The results of predictions were, furthermore, compared to the ones generated by Pearl's stochastic simulation method [21], a probabilistic reasoning method that operates directly on the theoretical Bayesian networks. The comparisons consistently show that the results of predictions based on the induced networks would be comparable to those generated by Pearl's method, when reasoning in a variety of uncertain knowledge domains驴those that were simulated using the presumed theoretical probabilistic networks of different topologies. Moreover, our validation experiments also reveal that the comparable performance of the implication-network-based-reasoning method can be achieved with much less computational cost than Pearl's stochastic simulation method; specifically, in all our experiments, the ratio between the actual CPU time required by our method and that by Pearl's is approximately 1:100.