Decomposable dependency models possess a number of interesting and useful proper-ties. This paper presents new characterizations of decomposable models in terms of in-dependence relationships, which are obtained by adding a single axiom to the well-known set characterizing dependency models that are isomorphic to undirected graphs. We also brie y discuss a potential application of our results to the problem of learning graphical models from data. 1
A class of log-linear models, referred to as labelled graphical models (LGMs), is introduced for mul...
This theoretical paper is inspired by an integer linear programming (ILP) approach to learning the s...
Graphical models (GMs) define a family of mathematical models aimed at the concise description of mu...
Decomposable dependency models possess a number of interesting and useful properties. This paper pre...
summary:We compare alternative definitions of undirected graphical models for discrete, finite varia...
Decomposable models are a subset of undirected graphical models that are built from triangulate
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys ...
Recent work on graphical models for relational data has demonstrated significant improvements in cla...
Graphical models are a way of representing the relationships between features (variables). There are...
AbstractOne dilemma in the database community is the great variety of data models existing. We defin...
AbstractCombinatorial properties of dependence graphs are considered. In particular, a characterizat...
Instance independence is a critical assumption of tra-ditional machine learning methods contradicted...
We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDG...
This work is centred on investigating dependencies and representing learned structures as graphs. W...
International audienceThis paper is concerned with learning in the model of Gold the Categorial Depe...
A class of log-linear models, referred to as labelled graphical models (LGMs), is introduced for mul...
This theoretical paper is inspired by an integer linear programming (ILP) approach to learning the s...
Graphical models (GMs) define a family of mathematical models aimed at the concise description of mu...
Decomposable dependency models possess a number of interesting and useful properties. This paper pre...
summary:We compare alternative definitions of undirected graphical models for discrete, finite varia...
Decomposable models are a subset of undirected graphical models that are built from triangulate
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys ...
Recent work on graphical models for relational data has demonstrated significant improvements in cla...
Graphical models are a way of representing the relationships between features (variables). There are...
AbstractOne dilemma in the database community is the great variety of data models existing. We defin...
AbstractCombinatorial properties of dependence graphs are considered. In particular, a characterizat...
Instance independence is a critical assumption of tra-ditional machine learning methods contradicted...
We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDG...
This work is centred on investigating dependencies and representing learned structures as graphs. W...
International audienceThis paper is concerned with learning in the model of Gold the Categorial Depe...
A class of log-linear models, referred to as labelled graphical models (LGMs), is introduced for mul...
This theoretical paper is inspired by an integer linear programming (ILP) approach to learning the s...
Graphical models (GMs) define a family of mathematical models aimed at the concise description of mu...