Results 21 to 30 of about 105,151 (260)

Variational Tobit Gaussian Process Regression

Statistics and Computing, 2023
AbstractWe propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically ...
Marno Basson, Tobias M. Louw, Theresa R. Smith   +2 more
openaire   +3 more sources

Gaussian Process Regression Networks [PDF]

, 2011
17 pages, 3 figures, 1 table.
Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani   +2 more
openaire   +3 more sources

Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller

IEEE Access, 2023
This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the
Hyuntae Kim, Hamin Chang
doaj   +1 more source

Gaussian Process Regression for Binned Data [PDF]

CoRR, 2018
10 pages (+1 supp), 4 ...
Smith, M.T., Álvarez, M.A., Lawrence, N.D.   +2 more
openaire   +3 more sources

Projection pursuit Gaussian process regression

IISE Transactions, 2022
A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable
Gecheng Chen, Rui Tuo
openaire   +2 more sources

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Applied Sciences, 2022
During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-
Mahmood Ahmad, Maaz Amjad, Ramez A. Al-Mansob, Paweł Kamiński, Piotr Olczak, Beenish Jehan Khan, Arnold C. Alguno   +6 more
doaj   +1 more source

Energy-Driven Image Interpolation Using Gaussian Process Regression

Journal of Applied Mathematics, 2012
Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing.
Lingling Zi, Junping Du
doaj   +1 more source

Barrier distribution extraction via Gaussian process regression [PDF]

EPJ Web of Conferences
This work presents a novel method for extracting potential barrier distributions from experimental fusion cross sections. We utilize a simple Gaussian process regression (GPR) framework to model the observed cross sections as a function of energy for ...
Godbey Kyle
doaj   +1 more source

A Gaussian Process Regression Framework for Gasification Process Outcome Forecasting [PDF]

Journal of Artificial Intelligence and System Modelling
The prediction of the products of the process is important for its evaluation and optimization. In the current research, a prediction algorithm framework based on a Gaussian Process Regression approach for predicting nitrogen and hydrogen content is ...
Sunil Kumar Malchi, Neelima Pannem, Suresh Babu Jugunta   +2 more
doaj   +1 more source

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, Amit Lath, Alexandre V. Morozov, Sindhu Murthy   +3 more
doaj   +1 more source