Probabilistic symmetries, extreme values and random topology

概率对称性、极值和随机拓扑

• 批准号: RGPIN-2020-04356
• 负责人: Shen, Yi
• 金额: $ 1.53万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716990
• 关键词: Probabilistic; symmetries; extreme; values; random

Models exhibiting certain distributional symmetries are important topics in probability, and have applications in various disciplines such as physics, astronomy, climatology and oceanography. Commonly used symmetries include stationarity (distributional invariance under translation), self-similarity (distributional invariance under scaling), isotropy (distributional invariance under rotation), etc. Literature shows that some symmetries can lead to specific behavior of the extreme values of the process/random field. Extremes, on the other hand, are closely related to the emerging research area of random topology. It studies the topological properties of random sets such as the excursion set (the area on which the value of a random field is larger than a threshold), and differs from the traditional approaches by focusing on the features which are robust under deformation. The goal of the proposed research is to develop a theory that reveals the relation among probabilistic symmetries, extreme values, and random topology. The research will be pursued as two closely related and interacting streams. The first is to study processes/random fields having probability symmetries defined on various spaces, especially their extremes. I plan to consider the spaces which are compact (S^1, S^n), discrete (Z), or normed differently (p-adic numbers). The second stream will study the topology of the excursion sets for the random fields having probabilistic symmetries. It is strongly motivated by some research in brain imaging, where the excursion sets correspond to the active regions in the brain. The existing results are either asymptotic or for the expected Euler characteristic. I plan to derive bounds for some other important topological quantities, such as the number of the connected components of the excursion sets, for finite thresholds. The proposed research program will contribute significantly in probability and statistics by providing a unified framework to study the extreme values and random topology under probabilistic symmetries. It will lead to a better understanding of the processes defined on different spaces and conversely, the characterization of the spaces using the random locations. Moreover, this will be the first work where bounds are derived for certain topological quantities of the excursion sets. The results are especially useful when topological features are used for data analysis, such as in climatology and oceanography. Finally, the results from the second stream can be directly applied to brain imaging, for example, to detect certain brain illness, so that the patients can get earlier and more precise diagnosis. Thus, these works have potential value for several sectors that are critical to the Canadian public. The students involved will acquire knowledge in probability and related areas as well as skills in problem solving and communication, which prepare them for future career developments.

表现出一定分布对称性的模型是概率论中的重要课题，在物理学、天文学、气候学和海洋学等学科中都有应用。常用的对称性包括平稳性（平移下的分布不变性）、自相似性（缩放下的分布不变性）、各向同性（旋转下的分布不变性）等。另一方面，极值与随机拓扑的新兴研究领域密切相关。它研究了随机集的拓扑性质，如漂移集（随机场值大于阈值的区域），与传统方法不同的是，它专注于变形下的鲁棒特征。 该研究的目标是建立一个理论，揭示概率对称性、极值和随机拓扑之间的关系。研究将作为两个密切相关和相互作用的流进行。第一个是研究具有定义在各种空间上的概率对称性的过程/随机场，特别是它们的极端。我打算考虑紧空间（S^1，S^n）、离散空间（Z）或赋不同范的空间（p-adic数）。第二部分将研究具有概率对称性的随机场的偏移集的拓扑。这是由脑成像中的一些研究强烈推动的，其中偏移集对应于大脑中的活动区域。现有的结果是渐近的或预期的欧拉特征。我计划推导出其他一些重要的拓扑量的界限，如在有限阈值下，行程集的连通分量的数目。 该研究计划将为概率统计学提供一个统一的框架来研究概率对称下的极值和随机拓扑，从而为概率统计学做出重大贡献。它将导致更好地理解定义在不同空间上的过程，反之，使用随机位置来表征空间。此外，这将是第一次的工作，其中的界限导出某些拓扑量的游览集。当拓扑特征用于数据分析时，例如在气候学和海洋学中，这些结果特别有用。最后，第二个流的结果可以直接应用于大脑成像，例如，检测某些大脑疾病，以便患者能够得到更早和更准确的诊断。因此，这些作品对加拿大公众至关重要的几个部门具有潜在价值。参与的学生将获得概率和相关领域的知识，以及解决问题和沟通的技能，为未来的职业发展做好准备。