A Bayesian/maximum-entropy view to the spatial estimation problem |
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| Authors: | George Christakos |
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| Institution: | (1) Department of Environmental Sciences and Engineering, The University of North Carolina, Rosenau Hall, CB 7400, 27599-7400 Chapel Hill, NC |
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| Abstract: | The purpose of this paper is to stress the importance of a Bayesian/maximum-entropy view toward the spatial estimation problem. According to this view, the estimation equations emerge through a process that balances two requirements: High prior information about the spatial variability and high posterior probability about the estimated map. The first requirement uses a variety of sources of prior information and involves the maximization of an entropy function. The second requirement leads to the maximization of a so-called Bayes function. Certain fundamental results and attractive features of the proposed approach in the context of the random field theory are discussed, and a systematic spatial estimation scheme is presented. The latter satisfies a variety of useful properties beyond those implied by the traditional stochastic estimation methods. |
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| Keywords: | spatial estimation entropy Bayes law information geostatistics |
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