Titlebook: Scalable Uncertainty Management; Second International Sergio Greco,Thomas Lukasiewicz Conference proceedings 2008 Springer-Verlag Berlin He

积聚

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




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




书目名称Scalable Uncertainty Management网络公开度




书目名称Scalable Uncertainty Management网络公开度学科排名




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




书目名称Scalable Uncertainty Management年度引用




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




书目名称Scalable Uncertainty Management读者反馈




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

构成

,Managing Probabilistic Data with MystiQ: The Can-Do, the Could-Do, and the Can’t-Do,r a re-implementation from scratch is that it can leverage the infrastructure of an existing database engine, e.g. indexes, query evaluation, query optimization, etc. Furthermore, MystiQ attempts to perform most of the probabilistic inference inside the relational database engine. MystiQ is currently available from ..

会议

Loopy Propagation in a Probabilistic Description Logic, this context, inference is equated with calculation of (bounds on) posterior probability in relational credal/Bayesian networks. As exact inference does not seem scalable due to the presence of quantifiers, we present first-order loopy propagation methods that seem to behave appropriately for non-trivial domain sizes.

结束

巫婆

Transitive Observation-Based Causation, Saliency, and the Markov Condition,antitative model, and a Saliency condition (if . is true then generally . is true) for the qualitative model. We explore the formal relations between these sufficient conditions, and between the underlying definitions of observation-based causation. These connections shed light on the range of applicability of both models.

Mammal

Tractable Reasoning with Bayesian Description Logics,tisfiability checking and answering unions of conjunctive queries in the new logics can be done in LogSpace in the data complexity. For this reason, the new probabilistic description logics are very promising formalisms for data-intensive applications in the Semantic Web involving probabilistic uncertainty.

流浪

话

An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs,ithm is particularly suitable for classification with uncertain inputs since it allows classification in polynomial time using different efficient transformations of initial naive possibilistic networks.

SUE

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