Add to ORKGIn this paper we present the R package gRc for statistical inference in graphical Gaussian models in which symmetry restrictions have been imposed on the concentration or partial correlation matrix. The models are represented by coloured graphs where parameters associated with edges or vertices of same colour are restricted to being identical. We describe algorithms for maximum likelihood estimation and discuss model selection issues. The paper illustrates the practical use of the gRc package
The R package BGGM provides tools for making Bayesian inference in Gaussian graphical models
Algebraic statistics exploits the use of algebraic techniques to develop new paradigms and algorithm...
We consider the Bayesian analysis of undirected graphical Gaussian models with edges and vertices sy...
In this paper we present the R package gRc for statistical inference in graphical Gaussian models in...
In this paper we present the R package gRc for statistical inference in graphical Gaussian models in...
Summary. We introduce new types of graphical Gaussian models by placing symmetry restrictions on the...
This thesis is concerned with graphical Gaussian models with equality constraints on the concentrati...
<div><p>We propose a model selection algorithm for high-dimensional clustered data. Our algorithm co...
In this paper we introduce restricted concentration models (RCMs) as a class of graphical models for...
Probabilistic graphical models provide a powerful framework for representing and reasoning about com...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentra...
We explore elliptical graphical models as a generalization of Gaussian graphical models, that is, we...
Gaussian graphical models have become a well-recognized tool for the analysis of conditional indepen...
The R package BGGM provides tools for making Bayesian inference in Gaussian graphical models
Algebraic statistics exploits the use of algebraic techniques to develop new paradigms and algorithm...
We consider the Bayesian analysis of undirected graphical Gaussian models with edges and vertices sy...
In this paper we present the R package gRc for statistical inference in graphical Gaussian models in...
In this paper we present the R package gRc for statistical inference in graphical Gaussian models in...
Summary. We introduce new types of graphical Gaussian models by placing symmetry restrictions on the...
This thesis is concerned with graphical Gaussian models with equality constraints on the concentrati...
<div><p>We propose a model selection algorithm for high-dimensional clustered data. Our algorithm co...
In this paper we introduce restricted concentration models (RCMs) as a class of graphical models for...
Probabilistic graphical models provide a powerful framework for representing and reasoning about com...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentra...
We explore elliptical graphical models as a generalization of Gaussian graphical models, that is, we...
Gaussian graphical models have become a well-recognized tool for the analysis of conditional indepen...
The R package BGGM provides tools for making Bayesian inference in Gaussian graphical models
Algebraic statistics exploits the use of algebraic techniques to develop new paradigms and algorithm...
We consider the Bayesian analysis of undirected graphical Gaussian models with edges and vertices sy...