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Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables. We propose an R package that provides an easy-to-use interface for multivariate GPR.
Oct 25, 2023
May 11, 2024 · Abstract. This article introduces funGp, an R package which handles regression problems involving multiple scalar and/or functional inputs, and a scalar output ...
Jun 3, 2024 · Gaussian Process Regression (GPR) is a powerful and flexible non-parametric regression technique used in machine learning and statistics. It is particularly ...
Jun 28, 2024 · Gaussian processes (GPs) are a class of stochastic (random) processes that are widely used for non-parametric regression, that is, when the relationship ...
Sep 13, 2023 · I am coding one, but the results just don't like as they should. Is there a resource out there that manually performs GP regression on a toy example? Like, do ...
Jun 9, 2024 · 2024-06-09. A Gaussian process is a stochastic process that assumes that the outputs for any set of input points follows a multivariate normal distribution. To ...
Oct 15, 2023 · Gaussian process regression (GPR) is a machine learning method based on non-parametric regression method that can be used to fit arbitrary scalar and vectorial ...
Mar 16, 2024 · I wrote a blogpost about when and why vanilla Gaussian Process regression fails to fit even a simple second-degree polynomial, trying out what the paper ...
Feb 1, 2024 · Gaussian Process Regression in scikit-learn, facilitated by the `GaussianProcessRegressor` class, excels in modelling complex relationships between input ...
Jun 10, 2024 · Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with ...