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Spatiotemporal modelling and parameter estimation of anisotropic particle trajectories

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STS - Statistical scalability

Trajectories of moving objects are collected everywhere and in massive volumes. In the first part of this talk I will present a framework for stochastically modelling such trajectories in time over two-dimensional space. I will show an application to modelling fluid particles in ocean turbulence, where trajectories are typically anisotropic due to spherical dynamics. In the second part, I will discuss computationally-efficient parameter estimation for massive datasets, where we propose an important modification to the Whittle likelihood to significantly reduce bias at no additional computational cost. We extend these estimation methods to spatiotemporal trajectories, such that we can estimate parameters that reveal the nature of the anisotropic structure.

This talk is part of the Isaac Newton Institute Seminar Series series.

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