Given a decomposable graph, we characterize and enumerate the set of pairs of vertices whose connection or disconnection results in a new graph that is also decomposable. We discuss the relevance of these results to Markov chain Monte Carlo methods that sample or optimize over the space of decomposable graphical models according to probabilities determined by a posterior distribution given observed multivariate data. © 2008 Elsevier B.V. All rights reserved
<p>The problem of finding densely connected subgraphs in a network has attracted a lot of recent int...
AbstractA graph G is H-decomposable if it can be expressed as an edge-disjoint union of subgraphs, e...
Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found...
Full Bayesian computational inference for model determination in undirected graphical models is curr...
Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable...
The junction-tree representation provides an attractive structural property for organising a decompo...
We derive methods for enumerating the distinct junction tree representations for any given decomposa...
This paper highlights an algorithm that computes, if possible, a nearly completely decomposable (NCD...
In this study we present a sequential sampling methodology for Bayesian inference in decomposable gr...
This paper presents a theoretical Monte Carlo Markov chain procedure in the framework of graphs. It ...
This paper deals with the Bayesian analysis of d-decomposable graphical models of marginal independ...
Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found...
© 2017 Biometrika Trust. We present a new kind of structural Markov property for probabilistic laws ...
We derive an explicit form of a Markov basis on the junction tree for a decomposable log-linear mode...
The learning of probability distributions from data is a ubiquitous problem in the fields of Statist...
<p>The problem of finding densely connected subgraphs in a network has attracted a lot of recent int...
AbstractA graph G is H-decomposable if it can be expressed as an edge-disjoint union of subgraphs, e...
Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found...
Full Bayesian computational inference for model determination in undirected graphical models is curr...
Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable...
The junction-tree representation provides an attractive structural property for organising a decompo...
We derive methods for enumerating the distinct junction tree representations for any given decomposa...
This paper highlights an algorithm that computes, if possible, a nearly completely decomposable (NCD...
In this study we present a sequential sampling methodology for Bayesian inference in decomposable gr...
This paper presents a theoretical Monte Carlo Markov chain procedure in the framework of graphs. It ...
This paper deals with the Bayesian analysis of d-decomposable graphical models of marginal independ...
Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found...
© 2017 Biometrika Trust. We present a new kind of structural Markov property for probabilistic laws ...
We derive an explicit form of a Markov basis on the junction tree for a decomposable log-linear mode...
The learning of probability distributions from data is a ubiquitous problem in the fields of Statist...
<p>The problem of finding densely connected subgraphs in a network has attracted a lot of recent int...
AbstractA graph G is H-decomposable if it can be expressed as an edge-disjoint union of subgraphs, e...
Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found...