spBPS - Bayesian Predictive Stacking for Scalable Geospatial Transfer
Learning
Provides functions for Bayesian Predictive Stacking within
the Bayesian transfer learning framework for geospatial
artificial systems, as introduced in "Bayesian Transfer
Learning for Artificially Intelligent Geospatial Systems: A
Predictive Stacking Approach" (Presicce and Banerjee, 2024)
<doi:10.48550/arXiv.2410.09504>. This methodology enables
efficient Bayesian geostatistical modeling, utilizing
predictive stacking to improve inference across spatial
datasets. The core functions leverage 'C++' for
high-performance computation, making the framework well-suited
for large-scale spatial data analysis in parallel and
distributed computing environments. Designed for scalability,
it allows seamless application in computationally demanding
scenarios.
