论文标题
非负强制性高斯流程回归
Nonnegativity-Enforced Gaussian Process Regression
论文作者
论文摘要
高斯工艺（GP）回归是一种用于近似复杂模型的灵活的非参数方法。在许多情况下，这些模型对应于具有有界物理特性的过程。标准的GP回归通常会导致代理模型，该模型对于所有时间或空间点都没有结合，因此留下了承担不可行的值的可能性。我们提出了一种方法，以在GP回归框架下以概率方式强制实施物理约束。此外，这种新方法减少了所得GP模型的差异。
Gaussian Process (GP) regression is a flexible non-parametric approach to approximate complex models. In many cases, these models correspond to processes with bounded physical properties. Standard GP regression typically results in a proxy model which is unbounded for all temporal or spacial points, and thus leaves the possibility of taking on infeasible values. We propose an approach to enforce the physical constraints in a probabilistic way under the GP regression framework. In addition, this new approach reduces the variance in the resulting GP model.
