A new class of graphs, chain graphs, suitable for modelling conditional independencies are
introduced and their Markov properties investigated. This class of graphs, which includes the …

Graphical Markov models use graphs to represent possible dependences among statistical
variables. Lauritzen, Wermuth, and Frydenberg (LWF) introduced a Markov property for …

Graphical Markov models use undirected graphs (UDGs), acyclic directed graphs (ADGs), or
(mixed) chain graphs to represent possible dependencies among random variables in a …

A chain graph (CG) is a graph admitting both directed and undirected edges with (partially)
directed cycles forbidden. It generalizes both the concept of undirected graph (UG) and the …

Undirected graphs and acyclic digraphs (ADG's), as well as their mutual extension to chain
graphs, are widely used to describe dependencies among variables in multiviarate …

Various special linear structures connected with covariance matrices are reviewed and
graphical methods for their representation introduced, involving in particular two different …

This paper aims to solve an often noted incompatibility between graphical chain models
which elucidate the conditional independence structure of a set of random variables and …

We use a close connection between the theory of Markov fields and that of log-linear
interaction models for contingency tables to define and investigate a new class of models for …

Log-linear statistical models are used to characterize random graphs with general
dependence structure and with Markov dependence. Sufficient statistics for Markov graphs …

Acyclic digraphs (ADGs) are widely used to describe dependences among variables in
multivariate distributions. In particular, the likelihood functions of ADG models admit …