Given a multinomial decomposable graphical model, we identify several alternative parametrizations; in particular we consider conditional probabilities of clique-residuals given separators, as well as generalized log-odds-ratios. For each such parametrization, we construct the corresponding reference prior for suitable groupings of the parameters. Each one of the reference priors we obtain is conjugate to the likelihood and is proper. Furthermore, all these priors are equivalent, in the sense that they can be deduced from each other by a change of variable. We also derive estimators of cell-probabilities based on the reference prior. Finally, we discuss in detail a parametrization associated to a collection of variables representing a ...
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improve...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, p...
The present paper considers discrete probability models with exact computational properties. In rela...
Given a multinomial decomposable graphical model, we identify several alternative parametrizations; ...
AbstractGiven a multinomial decomposable graphical model, we identify several alternative parametriz...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian ...
A class of log-linear models, referred to as labelled graphical models (LGMs), is introduced for mul...
Typically, statistical graphical models are either continuous and parametric (Gaussian, parameterize...
This paper addresses the task of eliciting an informative prior distribution for multinomial models....
The local specification of priors in non-decomposable graphical models does not necessarily yield a ...
We use a close connection between the theory of Markov fields and that of log-linear interaction mod...
This is a very interesting paper providing both theoretical and computational results for robust str...
In the framework of graphical models the graphical representation of the association structure is us...
Multinomial models arise when there is a set of complementary and mutually exclusive categories and ...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improve...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, p...
The present paper considers discrete probability models with exact computational properties. In rela...
Given a multinomial decomposable graphical model, we identify several alternative parametrizations; ...
AbstractGiven a multinomial decomposable graphical model, we identify several alternative parametriz...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian ...
A class of log-linear models, referred to as labelled graphical models (LGMs), is introduced for mul...
Typically, statistical graphical models are either continuous and parametric (Gaussian, parameterize...
This paper addresses the task of eliciting an informative prior distribution for multinomial models....
The local specification of priors in non-decomposable graphical models does not necessarily yield a ...
We use a close connection between the theory of Markov fields and that of log-linear interaction mod...
This is a very interesting paper providing both theoretical and computational results for robust str...
In the framework of graphical models the graphical representation of the association structure is us...
Multinomial models arise when there is a set of complementary and mutually exclusive categories and ...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improve...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, p...
The present paper considers discrete probability models with exact computational properties. In rela...