Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal li
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
We present a careful derivation of the Bayesian Inference Criterion (BIC) for model selection. The B...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
Model selection is an important problem in many branches including statistical analysis. In this the...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
Most researchers have specific expectations concerning their research questions. These may be derive...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
Most researchers have specific expectations concerning their research questions. These may be derive...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
We present a careful derivation of the Bayesian Inference Criterion (BIC) for model selection. The B...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
Model selection is an important problem in many branches including statistical analysis. In this the...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
Most researchers have specific expectations concerning their research questions. These may be derive...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
Most researchers have specific expectations concerning their research questions. These may be derive...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...