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We discuss a decision theoretic approach to learn causal Bayesian networks from observational data and experiments. We use the information of observational data to learn a completed partially directed acyclic graph using a structure learning technique and try to discover the directions of the remaining edges by means of experiment. We will show that our(More)
Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two well-known formalisms,(More)
With the rapid evolution of telecommunication networks, real-time network traffic management is becoming more and more crucial. We propose here a modular system for performing diagnosis at different levels of the network. It is designed as an aid to the operator. We present results of different experiments with a first version of this system which operates(More)
In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure-a tree-allows simple and efficient inference, while its latent(More)
Les réseaux bayésiens sont un formalisme de raisonnement probabiliste de plus en plus utilisé pour des tâches aussi diverses que le diagnostic médical, la fouille de texte ou encore la robotique. Dans certains cas, la structure du réseau bayésien est fournie a priori par un expert. Par contre, la détermination de cette structure à partir de données est une(More)
Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover(More)
BACKGROUND Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task. RESULTS We present an accurate modeling of dependences between genetic markers,(More)