We discuss a class of chain graph models for categorical variables defined by what we call a multivariate regression chain graph Markov property. First, the set of local independencies of these models is shown to be Markov equivalent to those of a chain graph model recently defined in the literature. Next we provide a parametrization based on a sequence of generalized linear models with a multivariate logistic link function that captures all independence constraints in any chain graph model of this kind
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
We present a new family of models that is based on graphs that may have undirected, directed and bid...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
GraphicalMap ov models use graphs, either undirected, directed, or mixed, to represent possible depe...
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
We present a new family of models that is based on graphs that may have undirected, directed and bid...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
We discuss a class of chain graph models for categorical variables defined by what we call a multiva...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
Graphical models are a useful tool with increasing diffusion. In the categorical variable framework,...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q vari...
GraphicalMap ov models use graphs, either undirected, directed, or mixed, to represent possible depe...
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dep...
We present a new family of models that is based on graphs that may have undirected, directed and bid...