Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them
In this thesis we explore the problem of inference for Bayesian model averaging. Many popular topics...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions ar...
MCMC sampling is a methodology that is becoming increasingly important in statistical signal process...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
a b s t r a c t We present an overview of Markov chain Monte Carlo, a sampling method for model infe...
Bayesian methods for variable selection and model choice have become increasingly popular in recent ...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of diff...
The standard methodology when building statistical models has been to use one of several algorithms ...
In this thesis we explore the problem of inference for Bayesian model averaging. Many popular topics...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions ar...
MCMC sampling is a methodology that is becoming increasingly important in statistical signal process...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
a b s t r a c t We present an overview of Markov chain Monte Carlo, a sampling method for model infe...
Bayesian methods for variable selection and model choice have become increasingly popular in recent ...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of diff...
The standard methodology when building statistical models has been to use one of several algorithms ...
In this thesis we explore the problem of inference for Bayesian model averaging. Many popular topics...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
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