论文标题
截短密度估计的得分匹配
Score Matching for Truncated Density Estimation on a Manifold
论文作者
论文摘要
当观察结果被截断时，我们仅限于数据集的不完整图片。最近的方法建议将得分匹配用于截断密度估计，其中不需要进入棘手的归一化常数。我们提出了截断得分的新颖扩展，该分数匹配与边界的riemannian歧管。在$ \ mathbb {r}^3 $中，在二维领域上为Von Mises-Fisher和Kent发行版提供了申请，以及在美国极端风暴观察的现实应用。在模拟数据实验中，我们的分数匹配估计器能够以较低的估计误差近似于真实的参数值，并显示出比天真最大似然估计器的改进。
When observations are truncated, we are limited to an incomplete picture of our dataset. Recent methods propose to use score matching for truncated density estimation, where the access to the intractable normalising constant is not required. We present a novel extension of truncated score matching to a Riemannian manifold with boundary. Applications are presented for the von Mises-Fisher and Kent distributions on a two dimensional sphere in $\mathbb{R}^3$, as well as a real-world application of extreme storm observations in the USA. In simulated data experiments, our score matching estimator is able to approximate the true parameter values with a low estimation error and shows improvements over a naive maximum likelihood estimator.
