Flexible Gaussian Processes via Convolution
نویسندگان
چکیده
Spatial and spatio-temporal processes are often described with a Gaussian process model. This model can be represented as a convolution of a white noise process and a smoothing kernel. We expand upon this model by considering convolutions of non-iid background processes. We highlight two particular models based on convolutions of Markov random fields and of time-varying processes. These models are illustrated using examples from hydrology and atmospheric science.
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