Acyclic directed mixed graphs (ADMGs) are graphs that contain directed (→) and bidirected
(↔) edges, subject to the constraint that there are no cycles of directed edges. Such graphs …

The present thesis deals with Gaussian conditional independence structures and their
inference problem. Conditional independence (CI) is a notion from statistics and information …

A gaussoid is a combinatorial structure that encodes independence in probability and
statistics, just like matroids encode independence in linear algebra. The gaussoid axioms of …

A graphical model encodes conditional independence relations via the Markov properties.
For an undirected graph these conditional independence relations can be represented by a …

Gaussian double Markovian models consist of covariance matrices constrained by a pair of
graphs specifying zeros simultaneously in the matrix and its inverse. We study the semi …

The gaussoid axioms are conditional independence inference rules which characterize
regular Gaussian CI structures over a three-element ground set. It is known that no finite set …

We study submodels of Gaussian DAG models defined by partial homogeneity constraints
imposed on the model error variances and structural coefficients. We represent these …

This chapter considers models based on some of the more common varieties of the mixed
graphs, specifically those introduced for two purposes: the unification of directed and …

In information theory, structural system constraints are frequently described in the form of a
directed acyclic graphical model (DAG). This paper addresses the question of classifying …

We study a class of conditional independence models for discrete data with the property that
one or more log-linear interactions are defined within two different marginal distributions and …