Validating Gaussian Process Models with Simulation-Based Calibration
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Abstract (arXiv)
Gaussian process priors are a popular choice for Bayesian analysis of regression problems. However, the implementation of these models can be complex, and ensuring that the implementation is correct can be challenging. In this paper we introduce Gaussian process simulation-based calibration, a procedure for validating the implementation of Gaussian process models and demonstrate the efficacy of this procedure in identifying a bug in existing code. We also present a novel application of this procedure to identify when marginalisation of the model hyperparameters is necessary.
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References · 7
- 2014Scalable Variational Gaussian Process Classificationcited by 228
- 2018Validating Bayesian Inference Algorithms with Simulation-Based Calibrationcited by 124
- 2018Yes, but Did It Work?: Evaluating Variational Inferencecited by 53
- 2020A Framework for Interdomain and Multioutput Gaussian Processescited by 48
- 2019Approximate Inference for Fully Bayesian Gaussian Process Regressioncited by 31
- 2020Marginalised Gaussian Processes with Nested Samplingcited by 7
- 2013Pseudo-Marginal Bayesian Inference for Gaussian Processescited by 5
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