Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox

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.

arXiv comment- Updated according to GPstuff 4.6. Added, e.g., Pareto smoothed importance sampling

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Cited by in corpus (7)

• Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC
• Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions
• Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
• Gaussian process regression for survival data with competing risks
• Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization
• Covariate dimension reduction for survival data via the Gaussian process latent variable model
• On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach