Add to ORKGThis paper develops a procedure based on Expected Posterior Priors to perform Bayesian model comparison for discrete undirected decomposable graphical models. The basic idea is that priors should not be assigned separately under each model; rather they should be related across models, in order to acquire some degree of compatibility, and thus allow fairer and more robust comparisons. The methodology is illustrated through the analysis of a 2 x 3 x 4 contingency table
Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more ge...
Different conditional independence specifications for ordinal categorical data are compared by calcu...
When two nested models are compared, using a Bayes factor, from an objective standpoint, two seemin...
This paper develops a procedure based on Expected Posterior Priors to perform Bayesian model compar...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, p...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian ...
Suppose we entertain Bayesian inference under a collection of models. This requires assigning a corr...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
Bayesian model comparison requires the specification of a prior distribution on the parameter space...
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requir...
Prior choice for Bayesian model comparison can be problematic for several reasons. In particular, fo...
Graphical model learning and inference are often performed using Bayesian techniques. In particular,...
Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more ge...
Different conditional independence specifications for ordinal categorical data are compared by calcu...
When two nested models are compared, using a Bayes factor, from an objective standpoint, two seemin...
This paper develops a procedure based on Expected Posterior Priors to perform Bayesian model compar...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, p...
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, ...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian ...
Suppose we entertain Bayesian inference under a collection of models. This requires assigning a corr...
We present a novel methodology for bayesian model determination in discrete decomposable graphical ...
Bayesian model comparison requires the specification of a prior distribution on the parameter space...
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requir...
Prior choice for Bayesian model comparison can be problematic for several reasons. In particular, fo...
Graphical model learning and inference are often performed using Bayesian techniques. In particular,...
Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more ge...
Different conditional independence specifications for ordinal categorical data are compared by calcu...
When two nested models are compared, using a Bayes factor, from an objective standpoint, two seemin...