Abstract:
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We introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the idea of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP regression model is fitted. A Bayesian additive framework combines the partitions, allowing the model to admit both global trends and local refinements on which a sparse GP construction enables efficient computation. The model addresses both the problem of efficiency in fitting a full Gaussian process regression model and the problem of prediction performance associated with a single sparse Gaussian process.
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