Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 8th European Confere Lluís Godo Conference proceedings 2005 Springer-Ve

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Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as possible in each case), the explanations are mutually exclusive, and the number of required expl

Trochlea

Alert Systems for Production Plants: A Methodology Based on Conflict Analysis energy consortium. During the course of the project we encountered several problems that we believe are common for projects of this type. Most notably, there was a lack of both knowledge and data concerning possible faults, and it therefore turned out to be infeasible to learn/construct a standard

glomeruli

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Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuitsl circuit. This model can be used to do probabilistic diagnosis based on manufacturer supplied information about nominal values of electrical components and their tolerances as well as measurements made on the circuit. Faulty components can be identified by looking for high probabilities for values

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Qualified Probabilistic Predictions Using Graphical Modelsprovide upper and lower bounds for the conditional probability associated with each predicted label. Empirical results confirm that FCVP can give well-calibrated predictions in online learning mode. Experimental results also show the prediction performance of FCVP is good in both the online and the

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A Decision-Based Approach for Recommending in Hierarchical Domainsa user profile. A general model for recommendation systems based on probabilistic graphical models is proposed in this paper. It is designed to deal with hierarchical domains, where the items can be grouped in a hierarchy, each item being only contained in another, more general item. The model makes

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