Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

TL;DR

This paper introduces a low-rank approximation method for Bayesian Gaussian processes using Laplace eigenfunctions, improving computational efficiency and providing practical guidelines for implementation in probabilistic programming.

Contribution

It offers a detailed analysis and practical recommendations for selecting basis functions and boundary factors in approximate Bayesian Gaussian processes, enhancing usability and performance.

Findings

The method achieves linear computational complexity.

Visualizations aid in selecting approximation parameters.

Demonstrated effectiveness in Stan probabilistic programming.

Abstract

Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intractable when the number of observations is large, especially when estimated with fully Bayesian methods such as Markov chain Monte Carlo. In this paper, we focus on a low-rank approximate Bayesian Gaussian processes, based on a basis function approximation via Laplace eigenfunctions for stationary covariance functions. The main contribution of this paper is a detailed analysis of the performance, and practical recommendations for how to select the number of basis functions and the boundary factor. Intuitive visualizations and recommendations, make it easier for users to improve approximation accuracy and computational performance. We also propose diagnostics for checking that the number of basis functions and the boundary factor are adequate given the data. The approach is simple and exhibits an attractive computational complexity due to its linear structure, and it is easy to implement in probabilistic programming frameworks. Several illustrative examples of the performance and applicability of the method in the probabilistic programming language Stan are presented together with the underlying Stan model code.