Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox

TL;DR

This paper introduces GPstuff, a MATLAB/Octave toolbox that simplifies the implementation of Gaussian process models, offering various inference methods, sparse approximations, and model assessment tools for practical probabilistic modeling.

Contribution

The paper presents GPstuff, a comprehensive toolbox that facilitates Gaussian process modeling with diverse inference techniques and practical features, enhancing usability and application scope.

Findings

Demonstrates GPstuff's effectiveness in multiple models

Provides a range of inference methods and approximations

Shows improved practicality of Gaussian process implementation

Abstract

Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the function. The inference can then be conducted directly in the function space by evaluating or approximating the posterior process. Despite their attractive theoretical properties GPs provide practical challenges in their implementation. GPstuff is a versatile collection of computational tools for GP models compatible with Linux and Windows MATLAB and Octave. It includes, among others, various inference methods, sparse approximations and tools for model assessment. In this work, we review these tools and demonstrate the use of GPstuff in several models.