# Validating Gaussian Process Models with Simulation-Based Calibration

arXiv: https://arxiv.org/abs/2110.15049 · HTML: https://mlsanity.com/paper/2110.15049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Validating Bayesian Inference Algorithms with Simulation-Based Calibration](https://mlsanity.com/paper/1804.06788/) (2018)
- [Yes, but Did It Work?: Evaluating Variational Inference](https://mlsanity.com/paper/1802.02538/) (2018)
- [A Framework for Interdomain and Multioutput Gaussian Processes](https://mlsanity.com/paper/2003.01115/) (2020)
- [Approximate Inference for Fully Bayesian Gaussian Process Regression](https://mlsanity.com/paper/1912.13440/) (2019)
- [Marginalised Gaussian Processes with Nested Sampling](https://mlsanity.com/paper/2010.16344/) (2020)
- [Pseudo-Marginal Bayesian Inference for Gaussian Processes](https://mlsanity.com/paper/1310.0740/) (2013)

## 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.