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Titlebook: Scalable Uncertainty Management; 9th International Co Christoph Beierle,Alex Dekhtyar Conference proceedings 2015 Springer International Pu

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Reasoning over Linear Probabilistic Knowledge Bases with Prioritiesnt on each level, there can be inconsistencies between different levels. Examples arise naturally in hierarchical domains when general knowledge is overwritten with more specific information. We extend recent results on inconsistency-tolerant probabilistic reasoning to propose a solution for this pr
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Multivariate Cluster-Based Discretization for Bayesian Network Structure Learningles are discrete and others are continuous is still an issue. A common way to tackle this problem is to preprocess datasets by first discretizing continuous variables and, then, resorting to classical discrete variable-based learning algorithms. However, such a method is inefficient because the cond
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Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Mod. The model’s unobserved states evolve according to the generalized Dirichlet conjugate prior distributions. The observations’ distribution is transformed into a set of Beta distributions each of which is re-parametrized as a unidimensional Dirichlet in its exponential form. We demonstrate that divi
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Linguistic and Graphical Explanation of a Cluster-Based Data Structure partitions are particularly suitable to define a subjective and domain dependent vocabulary that may then be used to personalize an information system. To make the translation of raw data into knowledge easier, we propose in this paper to generate personalized linguistic and graphical explanations
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Probability-Possibility Transformations: Application to Credal Networksnetwork into a possibilistic one. In particular, we are interested in satisfying some properties of probability-possibility transformations like dominance and order preservation. The second part of the paper deals with using probability-possibility transformations in order to perform MAP inference i
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