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Feb 22, 2024 · The objective is to develop a GP model that can accurately reflect the dynamics of temperature changes within a chemical reactor.
mgpr: An R package for multivariate Gaussian process regression
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Oct 25, 2023 · The aim of the mgpr package is to provide a GPR implementation tuned for multivariate regression problems of RS-based forest inventories.
May 11, 2024 · This article introduces funGp, an R package which handles regression problems involving multiple scalar and/or functional inputs, and a scalar output, ...
Jun 9, 2024 · A Gaussian process fits a model to a dataset, which gives a function that gives a prediction for the mean at any point along with a variance of this prediction.
Jun 3, 2024 · Gaussian Process Regression (GPR) is a powerful and flexible non-parametric regression technique used in machine learning and statistics.
Jan 2, 2024 · In this notebook we want to reproduce a classical example of using Gaussian processes to model time series data: The birthdays data set.
May 8, 2024 · The goal of mvgam is to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components.
Sep 10, 2023 · Gaussian Process regression is a function approximation method that obtains the poste- rior distribution of a clean process (i.e., with additive noise ...
Aug 28, 2024 · In the natural sciences, Gaussian processes have found use as probabilistic models of astronomical time series and as predictors of molecular properties.
Jan 26, 2024 · This study introduces a novel method for predicting drilling pressure in bolt support systems by optimizing Gaussian process time series regression (GPR) ...