Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment
Applications
2018-05-01 v2 Other Statistics
Abstract
We introduce a framework for updating large scale geospatial processes using a model-data synthesis method based on Bayesian hierarchical modelling. Two major challenges come from updating large-scale Gaussian process and modelling non-stationarity. To address the first, we adopt the SPDE approach that uses a sparse Gaussian Markov random fields (GMRF) approximation to reduce the computational cost and implement the Bayesian inference by using the INLA method. For non-stationary global processes, we propose two general models that accommodate commonly-seen geospatial problems. Finally, we show an example of updating an estimate of global glacial isostatic adjustment (GIA) using GPS measurements.
Cite
@article{arxiv.1804.06285,
title = {Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment},
author = {Zhe Sha and Jonathan Rougier and Maike Schumacher and Jonathan Bamber},
journal= {arXiv preprint arXiv:1804.06285},
year = {2018}
}