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

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书目名称Scalable Uncertainty Management影响因子(影响力)




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书目名称Scalable Uncertainty Management网络公开度




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书目名称Scalable Uncertainty Management被引频次




书目名称Scalable Uncertainty Management被引频次学科排名




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书目名称Scalable Uncertainty Management年度引用学科排名




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书目名称Scalable Uncertainty Management读者反馈学科排名

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0302-9743 Management, SUM 2015, held in Québec City, QC, Canada, in September 2015. The 25 regular papers and 3 short papers were carefully reviewed and selected from 49 submissions. The call for papers for SUM 2015 solicited submissions in all areas of managing and reasoning with substantial and complex kind

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The Mysterious World of Normal Numbers is known since 1909 that almost all real numbers are normal in every base .. This is one of the many reasons why the study of normal numbers has fascinated mathematicians for the past century. We present here a brief survey of some of the important results concerning normal numbers.

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DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Modelcription logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effort. We show this by reductions to edge covering problems, and derive a result of independent interest; namely, that counting edge covers in a particular class of graphs requires polynomial effort.

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The Complexity of Plate Probabilistic Modelsilistic networks in a compact and intuitive manner. In this paper we investigate the combined, data and domain complexity of plate models, showing that they range from polynomial to .-complete to .-complete.

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DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Modeldo so, one can resort to constructs from description logics. In this paper we present a class of relational Bayesian networks based on the popular description logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effor

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State Space Search with Stochastic Costs and Risk Aversional scenarios impacting the entire set of transitions; it can also result from local random factors impacting each transition independently, or from more complex combinations of these two cases. This leads us to consider three different settings for handling cost uncertainty in state space graphs. Fo