# Pseudo-Marginal Bayesian Inference for Gaussian Processes

@article{Filippone2014PseudoMarginalBI, title={Pseudo-Marginal Bayesian Inference for Gaussian Processes}, author={Maurizio Filippone and Mark A. Girolami}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2014}, volume={36}, pages={2214-2226} }

The main challenges that arise when adopting Gaussian process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on out-of-sample data. Using probit regression as an illustrative working example, this paper presents a general and effective methodology based on the pseudo-marginal approach to Markov chain Monte Carlo that efficiently addresses both of these issues. The results…

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