Improved Laplace Approximation for Marginal Likelihoods

Abstract

Statistical applications often involve the calculation of intractable multidimensional integrals. The Laplace formula is widely used to approximate such integrals. However, in high-dimensional or small sample size problems, the shape of the integrand function may be far from that of the Gaussian density, and thus the standard Laplace approximation can be inaccurate. We propose an improved Laplace approximation that reduces the asymptotic error of the standard Laplace formula by one order of magnitude, thus leading to third-order accuracy. We also show, by means of practical examples of various complexity, that the proposed method is extremely accurate, even in high dimensions, improving over the standard Laplace formula. Such examples also demonstrate that the accuracy of the proposed method is comparable with that of other existing methods, which are computationally more demanding. An R implementation of the improved Laplace approximation is also provided through the R package iLaplace available on CRAN.

24 pages

References in corpus (4)

• Distributions generated by perturbation of symmetry with emphasis on a multivariate skew $t$ distribution
• Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
• Monte Carlo likelihood inference for missing data models
• A note on marginal posterior simulation via higher-order tail area approximations

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