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Local Inversion-Free covariance estimation for Gaussian Spatial Processes

Friday, September 22, 2017 - 9:30am - 11:00am
Lind 409
Hossein Keshavarz (University of Minnesota, Twin Cities)
Gaussian processes provide a powerful tool for modelling the spatial dependence patterns. Evaluating the log-likelihood function of Gaussian process data, which is crucial for estimating unknown covariance parameters ,can be computationally intractable, particularly for large and irregularly spaced observations. We build a broad family of surrogate loss functions based on local moment-matching and a block diagonal approximation of the covariance matrix. This class of algorithms provides a versatile balance between the estimation accuracy and the computational cost. The fixed domain asymptotic behaviour of the proposed method is thoroughly studied for the isotropic Matern processes observed on a multi-dimensional irregular lattice.