Scaling Black-Box Inference to Large Spatial Settings: a Distributed Approach

Oregon Convention Center 

Extreme environmental processes display spatial and temporal dependencies that are computationally expensive to model, even with small datasets. These data are usually modeled using max stable models, and typical estimators rely on sub-optimal composite likelihoods that imply a loss in efficiency for higher dimensions. We propose a novel distributed deep learning multi-step approach. First, deep neural networks are trained using simulated data on subsets of the spatial domain to estimate parameters locally and quantify their uncertainty. We take advantage of the fact that simulation from such models is fast and easy in smaller spatial partitions. In the next step, a meta-estimator that reduces the bias of parameter estimates over a full data approach without compromising the increase of the variance that would follow is obtained. The proposed methodology enables statistical inference for intractable likelihoods with a previously prohibitive number of observations.