Random fields: non-Gaussian stochastic models and approximation schemes. The project aims to address important problems in the theory and statistics of stochastic processes and develop new methodology

Description

Random fields: non-Gaussian stochastic models and approximation schemes. 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.. Scheme: Discovery Projects. Field: 0104 - Statistics. Lead: A/Prof Andriy Olenko