Random fields: non-Gaussian stochastic models and approximation schemes

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。该项目期望产生关于在时空数据建模中使用的多维空间和表面上定义的随机过程的新知识。主要预期成果包括

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.