Multidimensional Laplace formulas for nonlinear Bayesian estimation

The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide...

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Musso, C.
Le Gland, F.
description The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute posterior expectations and variances have been proposed by Tierney, Kass and Kadane (1989). We provide in this article formulas for the multidimensional case. We demonstrate the accuracy of these formulas and show how to use them in importance sampling to design an importance density function which reduces the Monte Carlo error.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Approximation methods
Bayesian methods
Computational modeling
importance sampling
Laplace method
Monte Carlo methods
Nonlinear Bayesian estimation
Numerical models
Optimized production technology
Probability density function
title Multidimensional Laplace formulas for nonlinear Bayesian estimation
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