Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
This provides a software solution for researchers and practitioners using Gaussian processes in Bayesian modeling, but it is incremental as it reviews and packages existing tools.
The paper tackles the practical challenges of implementing Gaussian processes (GP) in Bayesian modeling by introducing GPstuff, a versatile toolbox for MATLAB and Octave, and demonstrates its application in several models.
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.