使INLA-SPDEPyMC3,.

Yujian Yang1, Xueqin Tong2

  • 1School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo, 255000, Shandong Province, China. yyjtshkh@126.com.

Scientific reports
|October 12, 2024
PubMed
概括

本研究开发了一个基于数据的模型,以了解冬季小麦的土壤体积含水量 (SVMC) 的空间变化和不确定性. 使用INLA-SPDE的贝叶斯推理提供了可靠的预测,并量化了不确定性,提高了湿度预测的准确性.

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