Learning Non-Gaussian Spatial Distributions Via Bayesian Transport Maps with Parametric Shrinkage

Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial field based on relatively few training samples. To address this challenge, a recently proposed Bayesian transport map (BTM) approach consists of a triangular transport map with nonparametric Gaussian-process components, which is trained to transform the distribution of interest to a Gaussian reference distribution. To improve the performance of this existing BTM, we propose to shrink the map components toward a "base" parametric Gaussian family combined with a Vecchia approximation for scalability. The resulting ShrinkTM approach is more accurate than the existing BTM, especially for small numbers of training samples. It can even outperform the "base" family when trained on a single sample of the spatial field. We demonstrate the advantage of ShrinkTM through numerical experiments on simulated data and on climate-model output.