Add to ORKGIn this paper we discuss a class of models of marginal independence for a set of categorical variables called discrete covariance graph models. These models, introduced by Drton & Richardson (2005) in the binary case, are a useful addition to standard graphical log-linear models which are represented by an undirected graph and encode conditional independencies, Lauritzen (1996)
We propose a conjugate and conditional conjugate Bayesian analysis of models of marginal independen...
Models defined by a set of conditional independence restrictions play an important role in statistic...
This paper introduces a novel class of models for binary data, which we call log-mean linear models....
In this paper we discuss a class of models of marginal independence for a set of categorical variab...
Undirected graphical models for categorical data represent a set of conditional independencies betw...
Graphical Markov models are multivariate statistical models in which the joint distribution satis\ua...
We extend the log-mean linear parameterization for binary data to discrete variables with arbitrary ...
Log-linear models are a classical tool for the analysis of contingency tables. In particular, the su...
We discuss two parameterizations of models for marginal independencies for dis-crete distributions w...
This chapter is devoted to graphical models in which the observed variables are categorical, that is...
We discuss two parameterizations of models for marginal independencies for discrete distributions wh...
This work compares the performances of three parameterizations for defining parsimonious submodels o...
This contribution aims to show how the log-mean linear parameterization for a set of categorical dat...
Traditional graphical models are extended by allowing that the presence or absence of a connection b...
We propose a conjugate and conditional conjugate Bayesian analysis of models of marginal independen...
Models defined by a set of conditional independence restrictions play an important role in statistic...
This paper introduces a novel class of models for binary data, which we call log-mean linear models....
In this paper we discuss a class of models of marginal independence for a set of categorical variab...
Undirected graphical models for categorical data represent a set of conditional independencies betw...
Graphical Markov models are multivariate statistical models in which the joint distribution satis\ua...
We extend the log-mean linear parameterization for binary data to discrete variables with arbitrary ...
Log-linear models are a classical tool for the analysis of contingency tables. In particular, the su...
We discuss two parameterizations of models for marginal independencies for dis-crete distributions w...
This chapter is devoted to graphical models in which the observed variables are categorical, that is...
We discuss two parameterizations of models for marginal independencies for discrete distributions wh...
This work compares the performances of three parameterizations for defining parsimonious submodels o...
This contribution aims to show how the log-mean linear parameterization for a set of categorical dat...
Traditional graphical models are extended by allowing that the presence or absence of a connection b...
We propose a conjugate and conditional conjugate Bayesian analysis of models of marginal independen...
Models defined by a set of conditional independence restrictions play an important role in statistic...
This paper introduces a novel class of models for binary data, which we call log-mean linear models....