A Markov Reward Process-Based Approach to Spatial Interpolation
@article{Arp2021AMR,
title={A Markov Reward Process-Based Approach to Spatial Interpolation},
author={Laurens Arp},
journal={ArXiv},
year={2021},
volume={abs/2106.00538}
}The interpolation of spatial data can be of tremendous value in various applications, such as forecasting weather from only a few measurements of meteorological or remote sensing data. Existing methods for spatial interpolation, such as variants of kriging and spatial autoregressive models, tend to suffer from at least one of the following limitations: (a) the assumption of stationarity, (b) the assumption of isotropy, and (c) the trade-off between modelling local or global spatial interaction…
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