Joint leave-group-out scoring of spatial predictions improves Bayesian spatial model selection accuracy and reduces variability compared to pointwise scoring when spatial dependence is strong.
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Joint leave-group-out cross-validation in Bayesian spatial models
Joint leave-group-out scoring of spatial predictions improves Bayesian spatial model selection accuracy and reduces variability compared to pointwise scoring when spatial dependence is strong.