Composite Gaussian Processes: Scalable Computation and Performance Analysis

TL;DR

This paper introduces a scalable composite Gaussian process model that uses a composite likelihood approach for efficient prediction and hyper-parameter learning, with analysis and validation on synthetic and real data.

Contribution

It proposes a novel composite likelihood-based approximation for Gaussian processes enabling scalable computation and provides theoretical and empirical analysis of its performance.

Findings

Effective approximation for large datasets

Recursive computation of predictors and hyper-parameters

Validated on synthetic and real-world data

Abstract

Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based on a composite likelihood approach using a general belief updating framework, which leads to a recursive computation of the predictor as well as of learning the hyper-parameters. We then provide an analysis of the derived composite GP model in predictive and information-theoretic terms. Finally, we evaluate the approximation with both synthetic data and a real-world application.