FastGP: An R Package for Gaussian Processes

Giri Gopalan; Luke Bornn

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arXiv · 1507.06055

FastGP: An R Package for Gaussian Processes

Giri Gopalan·Luke Bornn

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Abstract

Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we discuss a short R package for efficient multivariate normal functions which uses the Rcpp and RcppEigen packages at its core. GPs have properties that allow standard functions to be sped up; as an example we include functionality for Toeplitz matrices whose inverse can be computed in O(n^2) time with methods due to Trench and Durbin (Golub & Van Loan 1996), which is particularly apt when time points (or spatial locations) of a Gaussian process are evenly spaced, since the associated covariance matrix is Toeplitz in this case. Additionally, we include functionality to sample from a latent variable Gaussian process model with elliptical slice sampling (Murray, Adams, & MacKay 2010).

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Giri Gopalan, Luke Bornn. 2015-07-22. FastGP: An R Package for Gaussian Processes. https://arxiv.org/abs/1507.06055

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