Probabilistic regression models typically use the Maximum Likelihood Estimation or Cross-Validation to fit parameters. Unfortunately, these methods may give advantage to the solutions that fit observations in average, but they do not pay attention to the coverage and the width of Prediction Intervals. In this paper, we address the question of adjusting and calibrating Prediction Intervals for Gaussian Processes Regression. First we determine the model's parameters by a standard Cross-Validation or Maximum Likelihood Estimation method then we adjust the parameters to assess the optimal type II Coverage Probability to a nominal level. We apply a relaxation method to choose parameters that minimize the Wasserstein distance between the Gaussian distribution of the initial parameters (Cross-Validation or Maximum Likelihood Estimation) and the proposed Gaussian distribution among the set of parameters that achieved the desired Coverage Probability.
翻译:概率回归模型通常使用最大相似度估计法或交叉估计法来适应参数。 不幸的是,这些方法可能有利于适合平均观测的解决方案,但并不注意预测间间隔的覆盖范围和宽度。 在本文中,我们讨论高斯进程回归的调整和校准预测间距问题。 首先,我们通过标准的跨度估计法或最大相似度估计法来确定模型的参数。 然后,我们调整参数,以评估最佳的第二类覆盖概率达到名义水平。 我们采用放松法选择参数,将最初参数(Cross-validation 或最大相似度估计法)和初步参数(Cross-validation 或最大相似度估计法)之间的瓦斯特斯坦距离降至最小,并在实现预期覆盖概率的一组参数之间拟议的高斯分布。