Search Results for author: Joseph Guinness

Found 7 papers, 5 papers with code

Scalable Gaussian-process regression and variable selection using Vecchia approximations

1 code implementation25 Feb 2022 Jian Cao, Joseph Guinness, Marc G. Genton, Matthias Katzfuss

Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty.

Variable Selection

Scaled Vecchia approximation for fast computer-model emulation

1 code implementation1 May 2020 Matthias Katzfuss, Joseph Guinness, Earl Lawrence

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings.

Gaussian Processes

Inverses of Matern Covariances on Grids

no code implementations26 Dec 2019 Joseph Guinness

We conduct a study of the aliased spectral densities of Mat\'ern covariance functions on a regular grid of points, providing clarity on the properties of a popular approximation based on stochastic partial differential equations; while others have shown that it can approximate the covariance function well, we find that it assigns too much power at high frequencies and does not provide increasingly accurate approximations to the inverse as the grid spacing goes to zero, except in the one-dimensional exponential covariance case.

Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation

no code implementations20 May 2019 Joseph Guinness

We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood.

Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering

1 code implementation24 Apr 2019 Halley L. Brantley, Joseph Guinness, Eric C. Chi

Through simulation studies and our motivating application to low cost air quality sensor data, we demonstrate that our model provides better quantile trend estimates than existing methods and improves signal classification of low-cost air quality sensor output.

Methodology Applications Computation

Vecchia approximations of Gaussian-process predictions

1 code implementation8 May 2018 Matthias Katzfuss, Joseph Guinness, Wenlong Gong

Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasible for large datasets.

Methodology Computation

A general framework for Vecchia approximations of Gaussian processes

1 code implementation21 Aug 2017 Matthias Katzfuss, Joseph Guinness

Gaussian processes (GPs) are commonly used as models for functions, time series, and spatial fields, but they are computationally infeasible for large datasets.

Methodology Computation

Cannot find the paper you are looking for? You can Submit a new open access paper.