A Bayesian network is a graphical model that encodesprobabilistic relationships among
variables of interest. When used inconjunction with statistical techniques, the graphical …

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 …

En las dos últimas décadas se ha producido un notable desarrollo en el área de la
inteligencia artificial y, en particular, en la de los sistemas expertos. Debido a su carácter …

Since the development of Quetelet's homme moyen in the early nineteenth century, social
scientists have employed statistical concepts and methods to help them understand their …

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

The class of chain graphs (CGs) involving both undirected graphs (= Markov networks) and
directed acyclic graphs (= Bayesian networks) was introduced in middle eighties for …

The concept of d-separation (Pearl, 1988) was originally defined for acyclic directed graphs,
but there is a natural extension of the concept to directed graphs with cycles. When exactly …

Belief networks are graphic structures capable of representing dependence and
independence relationships among variables in a given domain of knowledge. We focus on …

Lattice conditional independence (LCI) models for multivariate normal data recently have
been introduced for the analysis of non-monotone missing data patterns and of nonnested …

In this dissertation, I address problems with the standard practice of model selection, which
does not account for the uncertainty in the model selection procedure. Using Bayesian …