Zellner’s g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to compare the mixture formulation with fixed g priors, empirical Bayes approaches, and other default procedures
This paper deals with Bayesian inference of a mixture of Gaussian dis-tributions. A novel formulatio...
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for ...
<p>The adoption of Zellner's g prior is a popular prior choice in Bayesian Model Averaging, although...
© 2017 Elsevier B.V. Recently, Bayesian procedures based on mixtures of g-priors have been widely st...
We examine the issue of variable selection in linear regression modelling, where we have a potential...
Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to...
Abstract: This paper studies Bayesian variable selection in linear models with general spherically s...
We present a Bayesian variable selection method based on an extension of the Zellner\u27s g-prior in...
<p>Mixtures of Zellner’s <i>g</i>-priors have been studied extensively in linear models and have bee...
AbstractWe examine the issue of variable selection in linear regression modelling, where we have a p...
© 2017 International Society for Bayesian Analysis. We consider Bayesian approaches for the hypothes...
I congratulate the authors of this very interesting paper on their work in which they implement my s...
While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for ...
This paper deals with Bayesian inference of a mixture of Gaussian dis-tributions. A novel formulatio...
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for ...
<p>The adoption of Zellner's g prior is a popular prior choice in Bayesian Model Averaging, although...
© 2017 Elsevier B.V. Recently, Bayesian procedures based on mixtures of g-priors have been widely st...
We examine the issue of variable selection in linear regression modelling, where we have a potential...
Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to...
Abstract: This paper studies Bayesian variable selection in linear models with general spherically s...
We present a Bayesian variable selection method based on an extension of the Zellner\u27s g-prior in...
<p>Mixtures of Zellner’s <i>g</i>-priors have been studied extensively in linear models and have bee...
AbstractWe examine the issue of variable selection in linear regression modelling, where we have a p...
© 2017 International Society for Bayesian Analysis. We consider Bayesian approaches for the hypothes...
I congratulate the authors of this very interesting paper on their work in which they implement my s...
While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for ...
This paper deals with Bayesian inference of a mixture of Gaussian dis-tributions. A novel formulatio...
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for ...
<p>The adoption of Zellner's g prior is a popular prior choice in Bayesian Model Averaging, although...