The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can b...

Assessing entity (e.g., person) risk from entity-related events requires appropriate techniques to address the relevance of events (individually and/or in aggregate) relative to a prevailing temporal frame of reference—for continuous risk monitoring, a running time point representing “the present.” We describe two classes of temporal relevance techniques we have used towards insider threat dete...

The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can b...

This paper is the second part of an energetic upscaling strategy to simulate grain growth at macroscopic scale with state variables that contain statistical descriptors structure. The first was dedicated derivation a fast mesoscopic model based on orientated tessellation updating method, which consists in succession Voronoi-Laguerre tessellations obtained by establishing evolution law directly ...

In this work we are concerned with the conceptual design of large-scale diagnostic and health management systems that use Bayesian networks. While they are potentially powerful, improperly designed Bayesian networks can result in too high memory requirements or too long inference times, to they point where they may not be acceptable for real-time diagnosis and health management in resource-boun...

-Possibilistic logic and Bayesian networks have provided advantageous methodologies and techniques for computerbased knowledge representation. This paper proposes a framework that combines these two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their ...

One of the main problems in credit risk management is the correlated default. In large portfolios, computing the default dependencies among issuers is an essential part in quantifying the portfolio's credit. The most important problems related to credit risk management are understanding the complex dependence structure of the associated variables and lacking the data. This paper aims at introdu...