Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
From MaRDI portal
Publication:6333706
arXiv2001.10818MaRDI QIDQ6333706
M. Girolami, François-Xavier Briol, George Wynne
Publication date: 29 January 2020
Abstract: Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultaneously quantifying uncertainty. However, this is only true when the model is well-specified, which is often not the case in practice. In this paper, we study the properties of Gaussian process means when the smoothness of the model and the likelihood function are misspecified. In this setting, an important theoretical question of practial relevance is how accurate the Gaussian process approximations will be given the difficulty of the problem, our model and the extent of the misspecification. The answer to this problem is particularly useful since it can inform our choice of model and experimental design. In particular, we describe how the experimental design and choice of kernel and kernel hyperparameters can be adapted to alleviate model misspecification.
This page was built for publication: Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6333706)
