算法线性受限高斯过程

Jan, 2018

Algorithmic Linearly Constrained Gaussian Processes

Markus Lange-Hegermann

TL;DR本文提出了一种基于 Gr"obner bases 算法构建满足线性微分方程的多输出高斯过程先验分布的方法，并将其应用于物理、地球数学和控制等多个领域，将随机学习和计算机代数学相结合，实现了噪声观测和精密计算的结合。

Abstract

We algorithmically construct multi-output gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all solutions of the equations using Gr\"obner bases. If success