This paper presents the aGrUM framework, a C++ library providing state-of-the-art
implementations of graphical models for decision making, including Bayesian Networks …

Construction projects do not often reach their expected results regarding time, cost, and
quality, due to the internal and external environment variations. Despite a substantial …

Tasks such as social network analysis, human behavior recognition, or modeling
biochemical reactions, can be solved elegantly by using the probabilistic inference …

We developed three distinct two-handed selection techniques for volumetric data
visualizations that use splat-based rendering. Two techniques are bimanual asymmetric …

Discovering causal relations in a knowledge base represents nowadays a challenging
issue, as it gives a brand new way of understanding complex domains. In this paper, we …

Probabilistic inference is among the main topics with reasoning in uncertainty in AI. For this
purpose, Bayesian Networks (BNs) is one of the most successful and efficient Probabilistic …

The production system and its maintenance system must be now developed on “system
thinking” paradigm in order to guarantee that Key Performance Indicators (KPI) will be …

Abstract Probabilistic Relational Models (PRMs) extend Bayesian networks (BNs) with the
notion of class of relational databases. Because of their richness, learning them is a difficult …

This paper presents experimental results of fast intrinsic evolutionary design and
evolutionary fault recovery of a 4-bit digital to analog converter (DAC) using the JPL stand …

The construction of Bayesian Networks (BNs) to model large-scale real-life problems is
challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs) …