Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

Water leakage in water distribution systems (WDSs) can bring various negative economic,
environmental, and safety effects. Therefore, predicting water leakage is one of the most …

The main challenge in develo** soft sensors in process industry is the existence of
irregularity of data, such as measurement noises, outliers, and missing data. This paper is …

Understanding the genetic regulatory networks, the discovery of interactions between genes
and understanding regulatory processes in a cell at the gene level are the major goals of …

We present new algorithms for learning Bayesian networks from data with missing values
using a data augmentation approach. An exact Bayesian network learning algorithm is …

Durant ces travaux de thèse, une comparaison empirique de différentes techniques
d'apprentissage de structure de réseaux bayésiens a été effectuée, car même s' il peut en …

Researchers have long attempted to identify factors that could explain why learning to
program is easier for some than the others. That is, the goal has been to determine what …

Cette thèse s' inscrit dans le cadre général des systèmes d'enseignement adaptatifs. La
problématique traitée est l'adaptation de l'activité pédagogique au mode d'apprentissage …

Cancer has complex patterns of progression that include converging as well as diverging
progressional pathways. Vogelstein's path model of colon cancer was a pioneering …

This paper proposes a new method, named Greedy Equivalence Search-Expectation
Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method …