Validating Gaussian Process Models with Simulation-Based Calibration
arXiv:2110.15049
Abstract
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.
arXiv comment2 pages, 1 figure
References in corpus (6)
- Scalable Variational Gaussian Process Classification
- Validating Bayesian Inference Algorithms with Simulation-Based Calibration
- Slice sampling covariance hyperparameters of latent Gaussian models
- Yes, but Did It Work?: Evaluating Variational Inference
- A Framework for Interdomain and Multioutput Gaussian Processes
- Marginalised Gaussian Processes with Nested Sampling