Chintan A. Dalal

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Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stationary covariance function has shown(More)
A comprehensive analysis of climate data can provide valuable information for weather forecasting and for our societys sustainable development. However, key challenges faced when modeling these climate data include the changing distribution of the underlying processes of the earths system, insufficient records, and unknown interacting physical processes.(More)
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