Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach

Music City Center 

Building artificially intelligent geospatial systems require rapid delivery of spatial data analysis at massive scales with minimal human intervention. Depending upon their intended use, data analysis may also entail model assessment and uncertainty quantification. This article devises transfer learning frameworks for deployment in artificially intelligent systems, where a massive data set is split into smaller data sets that stream into the analytical framework to propagate learning and assimilate inference for the entire data set. Specifically, we introduce Bayesian predictive stacking for multivariate and spatial data and demonstrate its effectiveness in rapidly analyzing massive data sets. Furthermore, we make inference feasible in a reasonable amount of time, and without excessively demanding hardware settings. We also discuss Bayesian predictive stacking for spatial-temporal models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of the hyper-parameters. We devise predictive stacking in a manner that is computationally efficient without resorting to iterative algorithms such as Markov chain Monte Carlo (MCMC) and can exploit the benefits of parallel computations. We illustrate the effectiveness of this approach in extensive simulation experiments and subsequently analyze massive data sets from climate science and wearable devices data.

GeoAI

Bayesian Transfer Learning

Predictive Stacking

Spatial-Temporal data