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Gaussian process

About: Gaussian process is a research topic. Over the lifetime, 18944 publications have been published within this topic receiving 486645 citations. The topic is also known as: Gaussian stochastic process.


Papers
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Journal ArticleDOI
TL;DR: An adaptation mechanism for a soft sensor based on a mixture of Gaussian process regression models is proposed and a procedure for input variable selection based on mutual information is presented, thus simplifying model development and adaptation.

145 citations

Journal ArticleDOI
TL;DR: The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling.
Abstract: This document describes the new features in version 2x of the tgp package for R, implementing treed Gaussian process (GP) models The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART), to treed Gaussian processes with jumps to the limiting linear model It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007)

145 citations

Journal ArticleDOI
TL;DR: The novel heuristic outperforms OptQuest in terms of number of simulated input combinations and quality of the estimated optimum and is compared with the popular commercial heuristic OptQuest embedded in the Arena versions 11 and 12.

145 citations

Journal ArticleDOI
TL;DR: Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding and with block classification based on AC energy, the densities of the DCT coefficients are much closer to the LaPLacian or even the Gaussian.
Abstract: Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding. A difference in peak signal to noise ratio (PSNR) of at most 0.5 dB is observed for encoding different images. We also compare maximum likelihood estimation of the generalized Gaussian density parameters with a simpler method proposed by Mallat (1989). With block classification based on AC energy, the densities of the DCT coefficients are much closer to the Laplacian or even the Gaussian. >

145 citations

Journal ArticleDOI
TL;DR: In this paper, an asymptotically-minimum-variance algorithm for estimating the MA (moving average) and ARMA (autoregressive moving average) parameters of non-Gaussian processes from sample high-order moments is given.
Abstract: A description is given of an asymptotically-minimum-variance algorithm for estimating the MA (moving-average) and ARMA (autoregressive moving-average) parameters of non-Gaussian processes from sample high-order moments. The algorithm uses the statistical properties (covariances and cross covariances) of the sample moments explicitly. A simpler alternative algorithm that requires only linear operations is also presented. The latter algorithm is asymptotically-minimum-variance in the class of weighted least-squares algorithms. >

144 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023502
20221,181
20211,132
20201,220
20191,119
2018978