Filling survey gaps in food security monitoring with spatio-temporal additive Gaussian process models
用时空加性高斯过程填补粮食安全监测的调查空缺
Ensuring food security across all regions of a country requires continuous monitoring, yet household surveys often leave significant spatio-temporal gaps due to resource constraints and operational priorities. In this paper, we propose a spatio-temporal additive Gaussian process model to estimate sub-national food security time series by regions. To address the computational cost of Gaussian process models, we exploit Kronecker structure of the spatio-temporal covariance…