Bayesian model selection and model averaging
Journal of Mathematical Psychology
On Bayesian model and variable selection using MCMC
Statistics and Computing
A Bayesian model selection method with applications
Computational Statistics & Data Analysis
Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models
Statistics and Computing
New approaches to compute Bayes factor in finite mixture models
Computational Statistics & Data Analysis
A Bayesian approach to model-based clustering for binary panel probit models
Computational Statistics & Data Analysis
Bayesian analysis of tail asymmetry based on a threshold extreme value model
Computational Statistics & Data Analysis
Threshold variable selection of asymmetric stochastic volatility models
Computational Statistics
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A range of approximate methods have been proposed for model choice based on Bayesian principles, given the problems involved in multiple integration in multi-parameter problems. Formal Bayesian model assessment is based on prior model probabilities P(M=j) and posterior model probabilities P(M=j|Y) after observing the data. An approach is outlined here that produces posterior model probabilities and hence Bayes factor estimates but not marginal likelihoods. It uses a Monte Carlo approximation based on independent MCMC sampling of two or more different models. While parallel sampling of the models is not necessary, such a form of sampling facilitates model averaging and assessing the impact of individual observations on the overall estimated Bayes factor. Three worked examples used before in model choice studies illustrate application of the method.