Bayesian sequential parameter estimation with a Laplace type approximation
Methodology
2015-09-29 v1 Computation
Abstract
A method for sequential inference of the fixed parameters of a dynamic latent Gaussian models is proposed and evaluated that is based on the iterated Laplace approximation. The method provides a useful trade-off between computational performance and the accuracy of the approximation to the true posterior distribution. Approximation corrections are shown to improve the accuracy of the approximation in simulation studies. A population-based approach is also shown to provide a more robust inference method.
Cite
@article{arxiv.1509.07900,
title = {Bayesian sequential parameter estimation with a Laplace type approximation},
author = {Tiep Mai and Simon Wilson},
journal= {arXiv preprint arXiv:1509.07900},
year = {2015}
}