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Block-GP: Scalable Gaussian Process Regression

Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence.

Software

This software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.3 or later.

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