نتایج جستجو برای: gaussian kriging

تعداد نتایج: 80763  

Journal: :International Journal of Geographical Information Science 2011
Guofeng Cao Phaedon C. Kyriakidis Michael F. Goodchild

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date...

2016
Chih-Li Sung Robert B. Gramacy Benjamin Haaland

Gaussian process models are commonly used as emulators for computer experiments. However, developing a Gaussian process emulator can be computationally prohibitive when the number of experimental samples is even moderately large. Local Gaussian process approximation (Gramacy and Apley, 2015) has been proposed as an accurate and computationally feasible emulation technique. Constructing sub-desi...

2000
JEF CAERS

Sequential Gaussian simulation (sgsim) and sequential indicator simulation (sisim) have emerged as powerful tools for stochastic imaging of Earth Science phenomena and are currently widely accepted fast simulation algorithms. In this paper, we will expand the sequential simulation toolbox by relying on a recent development in geostatistical theory (Journel, 1994) denoted as direct sequential si...

2013
Simo Särkkä Arno Solin

Gaussian process regression is a machine learning paradigm, where the regressor functions are modeled as realizations from an a priori Gaussian process model. We study abstract continuous-space Gaussian regression problems where the training set covers the whole input space instead of consisting of a finite number of distinct points. The model can be used for analyzing theoretical properties of...

Journal: :Journal of Machine Learning Research 2012
Chiwoo Park Jianhua Z. Huang Yu Ding

This paper presents the Getting-started style documentation for the local and parallel computation toolbox for Gaussian process regression (GPLP), an open source software package written in Matlab (but also compatible with Octave). The working environment and the usage of the software package will be presented in this paper.

2011
Manuel J. Marín-Jiménez Andrew Zisserman Vittorio Ferrari

The objective of this work is to determine if people are interacting in TV video by detecting whether they are looking at each other or not. We determine both the temporal period of the interaction and also spatially localize the relevant people. We make the following three contributions: (i) head pose estimation in unconstrained scenarios (TV video) using Gaussian Process regression; (ii) prop...

2017
Yi Ding Risi Kondor Jonathan Eskreis-Winkler

(a) (b) (c) Figure: (a) In a simple blocked low rank approximation the diagonal blocks are dense (gray), whereas the off-diagonal blocks are low rank. (b) In an HODLR matrix the low rank off-diagonal blocks form a hierarchical structure leading to a much more compact representation. (c) H2 matrices are a refinement of this idea. (a) In simple blocked low rank approximation the diagonal blocks a...

2017
Johan Wågberg Dave Zachariah Thomas B. Schön Petre Stoica

This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a...

Journal: :Journal of Machine Learning Research 2016
Rajarshi Guhaniyogi David B. Dunson

Nonparametric regression for large numbers of features (p) is an increasingly important problem. If the sample size n is massive, a common strategy is to partition the feature space, and then separately apply simple models to each partition set. This is not ideal when n is modest relative to p, and we propose an alternative approach relying on random compression of the feature vector combined w...

Journal: :European Journal of Operational Research 2016
Areski Cousin Hassan Maatouk Didier Rullière

Due to the lack of reliable market information, building financial term-structures may be associated with a significant degree of uncertainty. In this paper, we propose a new term-structure interpolation method that extends classical spline techniques by additionally allowing for quantification of uncertainty. The proposed method is based on a generalization of kriging models with linear equali...

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