Results 51 to 60 of about 374,385 (274)
Robust and Conjugate Gaussian Process Regression
CoRR, 2023 To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unreliable inferences and uncertainty quantification.
Matías Altamirano +2 more
openaire +3 more sources
Model selection and signal extraction using Gaussian Process regression
Journal of High Energy Physics, 2023 We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the ...
Abhijith Gandrakota +3 more
doaj +1 more source
Nonparametric regression analysis [PDF]
, 2015 textNonparametric regression uses nonparametric and flexible methods in analyzing complex data with unknown regression relationships by imposing minimum assumptions on the regression function.
Malloy, Shuling Guo
core +1 more source
Cross Trajectory Gaussian Process Regression Model for Battery Health Prediction
Journal of Modern Power Systems and Clean Energy, 2021 Accurate battery capacity prediction is important to ensure reliable battery operation and reduce the cost. However, the complex nature of battery degradation and the presence of capacity regeneration phenomenon render the prediction task very ...
Jianshe Feng +5 more
doaj +1 more source
Large-scale Heteroscedastic Regression via Gaussian Process
, 2020 Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics.
Cai, Jianfei, Liu, Haitao, Ong, Yew-Soon
core +1 more source
Sparse Additive Gaussian Process Regression
J. Mach. Learn. Res., 2019 In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Hengrui Luo +2 more
openaire +4 more sources
Gaussian Process Regression with Soft Equality Constraints
MathematicsThis study introduces a novel Gaussian process (GP) regression framework that probabilistically enforces physical constraints, with a particular focus on equality conditions.
Didem Kochan, Xiu Yang
doaj +1 more source
Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression
Energies, 2018 The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance.
Yu Peng +4 more
doaj +1 more source
Using Gaussian process regression for efficient parameter reconstruction
, 2019 Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical ...
Chisari +12 more
core +1 more source
Proprioceptive Robot Collision Detection through Gaussian Process Regression
, 2019 This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the ...
Alberto, Dalla Libera +4 more
core +1 more source