Reference : A Comparison of Bayesian Monte Carlo Markov Chain and Maximum Likelihood Estimation M...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/10993/12518
A Comparison of Bayesian Monte Carlo Markov Chain and Maximum Likelihood Estimation Methods for the Statistical Analysis of Geodetic Time Series
English
Olivares Pulido, German [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Teferle, Felix Norman mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
10-Dec-2013
No
No
International
American Geophysical Union Fall Meeting 2013
9-12-2013 to 13-12-2013
American Geophysical Union
San Francisco
USA
[en] Global Positioning System ; Uncertainty ; Monte Carlo Markov Chain ; Time Series Analysis
[en] One of the objectives of TIGA is to compute precise station coordinates and velocities for GPS stations of interest. Consequently, a comprehensive knowledge of the stochastic features of the GPS time series noise is crucial, as it affects the velocity estimation for each GPS station. For that, we present a Monte Carlo Markov Chain (MCMC) method to simultaneously estimate the velocities and the stochastic parameters of the noise in GPS time series. This method allows to get a sample of the likelihood function and thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. We propose this method as an alternative to optimization methods, such as the Maximum Likelihood Estimator (MLE) method implemented in the widely used CATS software, whenever the likelihood and the parameters of the noise are to be estimated in order to obtain more robust uncertainties for all parameters involved. Furthermore, we assess the MCMC method through comparison with the widely used CATS software using daily height time series from the Jet Propulsion Laboratory.
Researchers ; Professionals
http://hdl.handle.net/10993/12518

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