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Sequential , Bayesian Geostatistics : A Principled Method for Large Data Sets
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cornford, Dan Csató, Lehel Opper, Manfred |
| Abstract | The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to datasets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood based covariance estimation. Various ad-hoc approaches to solve this problem have been adopted, such as selecting a neighbourhood region and / or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this paper, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of ‘basis vectors’ which |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://publications.aston.ac.uk/10014/1/Cornford2005GA.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Entity Name Part Qualifier - adopted Estimated Hoc (programming language) Kriging Normal Statistical Distribution Sparse matrix Subgroup algorithm |
| Content Type | Text |
| Resource Type | Article |
