Add to ORKGThere are different models, which describe conditional independence induced by multivariate distributions. Models such as Undirected Graphs, Directed Acyclic Graphs, Essential Graphs and Annotated Graphs are introduced and compared in this thesis. The focus is put on annotated graphs. It is shown that annotated graphs represent equivalence classes of DAG-representable relations. An algorithm for reconstruction of an annotated graph from an essential graph as well as the algorithm for the inverse procedure are given. Some properties of a characteristic imset, which is a non-graphical representation, are discussed. A relationship between annotated graphs and characteristic imsets is investigated, an algorithm, which reconstructs an annotated ...
The class of chain graphs (CGs) involving both undirected graphs (= Markov networks) and directed ac...
Undirected graphical models for categorical data represent a set of conditional independencies betw...
Graphs provide an excellent framework for interrogating symmetric models of measurement random. vari...
There are different models, which describe conditional independence induced by multivariate distribu...
In this paper we consider conditional independence models closed under graphoid properties. We inves...
This paper introduces a class of graphical independence models that is closed under marginalization ...
AbstractIn this paper we study the problem of representing probabilistic independence models, in par...
In this thesis we describe subclasses of a class of graphs with three types of edges, called looples...
We consider graphs that represent pairwise marginal independencies amongst a set of variables (for i...
In this paper we study conditional independence structures arising from conditional probabilities an...
AbstractWhen it comes to learning graphical models from data, approaches based on conditional indepe...
In this paper we study the representation by means of an acyclic directed graph (DAG) of the indepen...
A graphical model encodes conditional independence relations via the Markov properties. For an undir...
In this paper, we propose an approach for structural learning of independence graphs from multiple d...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
The class of chain graphs (CGs) involving both undirected graphs (= Markov networks) and directed ac...
Undirected graphical models for categorical data represent a set of conditional independencies betw...
Graphs provide an excellent framework for interrogating symmetric models of measurement random. vari...
There are different models, which describe conditional independence induced by multivariate distribu...
In this paper we consider conditional independence models closed under graphoid properties. We inves...
This paper introduces a class of graphical independence models that is closed under marginalization ...
AbstractIn this paper we study the problem of representing probabilistic independence models, in par...
In this thesis we describe subclasses of a class of graphs with three types of edges, called looples...
We consider graphs that represent pairwise marginal independencies amongst a set of variables (for i...
In this paper we study conditional independence structures arising from conditional probabilities an...
AbstractWhen it comes to learning graphical models from data, approaches based on conditional indepe...
In this paper we study the representation by means of an acyclic directed graph (DAG) of the indepen...
A graphical model encodes conditional independence relations via the Markov properties. For an undir...
In this paper, we propose an approach for structural learning of independence graphs from multiple d...
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independen...
The class of chain graphs (CGs) involving both undirected graphs (= Markov networks) and directed ac...
Undirected graphical models for categorical data represent a set of conditional independencies betw...
Graphs provide an excellent framework for interrogating symmetric models of measurement random. vari...