A Markov equivalence class contains all the Directed Acyclic Graphs (DAGs) encoding the
same conditional independencies, and is represented by a Completed Partially Directed …

Markov models lie at the interface between statistical independence in a probability
distribution and graph separation properties. We review model selection and estimation in …

As the interdependence between arbitrary objects in the real world grows, it becomes
gradually important to use chain graphs containing directed and undirected edges to learn …

In this paper we study how different theoretical concepts of Bayesian networks have been
extended to chain graphs. Today there exist mainly three different interpretations of chain …

Learning an optimal chain graph from data is an important hard computational problem. We
present a new approach to solve this problem for various objective functions without making …

Learning the structure of dependence relations between variables is a pervasive issue in the
statistical literature. A directed acyclic graph (DAG) can represent a set of conditional …

Causal chain graphs model the dependency structure between individuals when the
assumption of individual independence in causal inference is violated. However, causal …

Graphical models based on Directed Acyclic Graphs (DAGs) are widely used to answer
causal questions across a variety of scientific and social disciplines. However, observational …

Chain graphs are graphs with possibly directed and undirected edges, and no semidirected
cycle. They have been extensively studied as a formalism to represent probabilistic …

Cette thèse présente une méthode pour la détection hors-ligne de multiples ruptures dans
des séries temporelles multivariées, et propose d'en exploiter les résultats pour estimer les …