Efficient-GP-Regression-via-Kalman-Filtering:通过迭代卡尔曼滤波实现有效的时空高斯过程回归的代码。 KF用于解析时空过程的时间部分,而标准GP回归用于空间部分

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通过卡尔曼滤波进行有效GP回归 基于两篇论文的存储库,其中包含相对于同类项目的简单实现代码: [1] A.Carron,M.Todescato,R.Carli,L.Schenato,G.Pillonetto,机器学习遇到了Kalman Filtering ,《 2016年第55届决策与控制会议论文集》,第4594-4599页。 [2] M.Todescato,A.Carron,R.Carli,G.Pillonetto,L.Schenato,通过卡尔曼滤波的有效时空高斯回归,ArXiv:1705.01485,已提交JMLR。 PS。 该代码尽管基于上述论文中使用的代码,但与之稍有不同。 它是它的后来的改进和简化版本。 而且,此处仍未提供[2]中介绍的用于实现自适应方法的代码。 文件内容是很容易解释的(有关每个文件的简要介绍,请参考相应的帮助): main.m:包含主程序 plotResul

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