对于高斯过程的推理,在紧的里曼的多元体上使用Matérn Covariogram
Didong Li1, Wenpin Tang2, Sudipto Banerjee3
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
概括
这项研究分析了对紧的里曼的多元体的高斯过程,重点关注马特恩的共存图. 它建立了参数识别能力,并证明了最大概率估计和预测器最佳性的一致性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 不同几何学微分几何学
背景情况:
- 斯过程对于模拟欧几里德空间中的复杂依赖关系至关重要.
- 研究正在扩展到非欧几里德的数据使用高斯过程在里曼的多样性.
- 在多元体上对共体图参数的异交推理仍然未得到充分探索.
研究的目的:
- 在紧的里曼的多元体上研究高斯过程的非对称行为.
- 在这些变频器上推导Matérn共振图的参数识别性.
- 建立统计推理方法的理论保证.
主要方法:
- 利用最近开发的马特恩共振图来对紧的里曼数组进行分析.
- 应用了对Matern Gaussian随机测量的等价性的正式条件.
- 在参数识别中利用微ergodicity的概念.
主要成果:
- 在紧的多元体上获得了Matérn高斯过程的可识别参数 (microergodic参数).
- 正式确定了最大概率估计的一致性.
- 证明了最好的线性无偏预测器的非对称最佳性.
结论:
- 这项研究为高斯过程推理在紧的里曼的多元体上提供了理论基础.
- 通过使用圆圈作为特定的多元体示例来证明其实际应用性.
- 数字实验验证了关于参数识别性和估计一致性的理论发现.
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