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Non-separable Non-stationary random fields

We describe a framework for constructing non-separable non-stationary random fields that is based on an infinite mixture of convolved stochastic processes. When the mixing process is stationary and the convolution function is non-stationary we arrive at expressive kernels that are available in closed form. When the mixing is non-stationary and the convolution function is stationary the resulting random fields exhibit varying degrees of non-separability that better preserve local structure. These kernels have natural interpretations through corresponding stochastic differential equations (SDEs) and are demonstrated on a range of synthetic benchmarks and spatio-temporal applications in geostatistics and machine learning. We show how a single Gaussian process (GP) with these kernels can computationally and statistically outperform both separable and existing non-stationary non-separable approaches such as treed GPs and deep GP constructions.

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