A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data

The paper introduces ResTGP, a Bayesian residual tree Gaussian Process framework designed for large, high‑dimensional spatial datasets with heterogeneous structures. It decomposes a Gaussian process across a dyadic tree, iteratively computing predictive and residual processes at each node to capture multi‑scale covariance efficiently. The method employs recursive message passing for linear‑time Bayesian inference and demonstrates posterior consistency, with numerical experiments and a storm surge application highlighting its advantages.

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