Gibbs sampler for computing and propagating large covariance matrices |
| |
| Authors: | Gundlich B Koch K-R Kusche J |
| |
| Institution: | (1) Mathematical Geodesy and Positioning, Delft University of Technology, Thijsseweg 11, 2629 JA Delft, The Netherlands;(2) Institute for Theoretical Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany;;(3) Physical, Geometrical, and Space Geodesy, Delft University of Technology, Thijsseweg 11, 2629 JA Delft, The Netherlands |
| |
| Abstract: | The use of sampling-based Monte Carlo methods for the computation and propagation of large covariance matrices in geodetic applications is investigated. In particular, the so-called Gibbs sampler, and its use in deriving covariance matrices by Monte Carlo integration, and in linear and nonlinear error propagation studies, is discussed. Modifications of this technique are given which improve in efficiency in situations where estimated parameters are highly correlated and normal matrices appear as ill-conditioned. This is a situation frequently encountered in satellite gravity field modelling. A synthetic experiment, where covariance matrices for spherical harmonic coefficients are estimated and propagated to geoid height covariance matrices, is described. In this case, the generated samples correspond to random realizations of errors of a gravity field model.
Acknowledgements The authors are indebted to Pieter Visser and Pavel Ditmar for providing simulation output that was used in the GOCE error generation experiments. Furthermore, the NASA/NIMA/OSU team is acknowledged for providing public ftp access to the EGM96 error covariance matrix. The two anonymous reviewers are thanked for their valuable comments. |
| |
| Keywords: | Covariance matrices Error propagation Gibbs sampler Monte Carlo Gravity field modelling |
| 本文献已被 SpringerLink 等数据库收录！ |
|
