Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator
نویسندگان
چکیده
The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. We describe the basic Laplace-Metropolis estimator for models without random eeects. For models with random eeects the compound Laplace-Metropolis estimator is introduced. This estimator is applied to data from the World Fertility Survey and shown to give accurate results. Batching of simulation output is used to assess the uncertainty involved in using the compound Laplace-Metropolis estimator. The method allows us to test for the eeects of independent variables in a random eeects model, and also to test for the presence of the random eeects.
منابع مشابه
ESTIl\1ATING BAYES FACTORS VIA POSTERIOR SIMULATION \VITH THE LAPLACE-lVIETROPOLIS ESTIlVIATOR
The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. vVe describe the basic LaplaceMetropolis estimator for models without random effects. For models w...
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