Random fields: non-Gaussian stochastic models and approximation schemes

随机场：非高斯随机模型和近似方案

• 批准号: DP220101680
• 负责人: A/Prof Andriy Olenko
• 金额: $ 30.64万
• 依托单位: La Trobe University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-08-30 至 2025-08-29
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220101680
• 关键词: Random; fields; non; Gaussian; stochastic

The project aims to address important problems in the theory and statistics of stochastic processes and develop new methodology for their applications. This project expects to generate new knowledge about stochastic processes defined on multidimensional spaces and surfaces that are used in spatio-temporal data modelling. Main anticipated outcomes include - developing approximation schemes for new complex data and investigating their accuracy and reliability; - studying nonlinear statistics and transformations of these data; - providing new tools to investigate complex real data, in particular, in cosmology and embryology. The results should provide significant benefits for optimal modelling and analysis of high resolution big data.

该项目旨在解决随机过程理论和统计中的重要问题，并为其应用开发新的方法。该项目预计将产生新的知识，随机过程定义的多维空间和表面，用于时空数据建模。主要预期成果包括 - 为新的复杂数据制定近似方案，并调查其准确性和可靠性; - 研究这些数据的非线性统计和变换; - 提供新的工具来研究复杂的真实的数据，特别是在宇宙学和胚胎学中。 这些结果将为高分辨率大数据的优化建模和分析提供显着的好处。