Accurate Computation of Marginal Data Densities Using Variational Bayes

Abstract: We propose a new marginal data density estimator (MDDE) that uses the variational Bayes posterior density as a weighting density of the reciprocal importance sampling (RIS) MDDE. This computationally convenient estimator is based on variational Bayes posterior densities that are available for many models and requires simulated draws only from the posterior distribution. It provides accurate estimates with a moderate number of posterior draws, has a finite variance, and provides a minimum variance candidate for the class of RIS MDDEs. Its reciprocal is consistent, asymptotically normally distributed, and unbiased. These properties are obtained without truncating the weighting density, which is typical for other such estimators. Our proposed estimators outperform many existing MDDEs in terms of bias and numerical standard errors. In particular, our RIS MDDE performs uniformly better than other estimators from this class.