Results 11 to 20 of about 257,461 (188)

DEMANDE: Density Matrix Neural Density Estimation

IEEE Access, 2023
Density estimation is a fundamental task in statistics and machine learning that aims to estimate, from a set of samples, the probability density function of the distribution that generated them.
Joseph A. Gallego-Mejia, Fabio A. Gonzalez   +1 more
doaj   +1 more source

Kernel Density Estimation Based Gaussian and Non-Gaussian Random Vibration Data Induction for High-Speed Train Equipment

IEEE Access, 2020
Because general statistics tolerance is not applicable to the induction of non-Gaussian vibration data and the methods for converting non-Gaussian data into Gaussian data are not always effective and can increase the estimation error, a novel kernel ...
Peng Wang, Hua Deng, Yi Min Wang, Yue Liu, Yi Zhang   +4 more
doaj   +1 more source

Kernel Density Derivative Estimation of Euler Solutions

Applied Sciences, 2023
Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity ...
Shujin Cao, Yihuai Deng, Bo Yang, Guangyin Lu, Xiangyun Hu, Yajing Mao, Shuanggui Hu, Ziqiang Zhu   +7 more
doaj   +1 more source

Varying kernel density estimation on [PDF]

Statistics & Probability Letters, 2012
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1-consistency are investigated.
Robert, Mnatsakanov, Khachatur, Sarkisian   +1 more
openaire   +2 more sources

Kernel density estimation via diffusion [PDF]

, 2010
We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate.
Botev, Z. I., Grotowski, J. F., Kroese, D. P.   +2 more
core   +2 more sources

An Assessment of Hermite Function Based Approximations of Mutual Information Applied to Independent Component Analysis

Entropy, 2008
At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation.
Julian Sorensen
doaj   +1 more source

Research of comparative analysis of nonparametric density estimation by applying Monte Carlo method

Lietuvos Matematikos Rinkinys, 2013
This paper presents nonparametric statistical estimation of distribution density. The Monte Carlo  method is used to show the effects of kernel function for multimodal kernel density estimation.
Indrė Drulytė, Tomas Ruzgas
doaj   +1 more source

Variable Kernel Density Estimation

The Annals of Statistics, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Terrell, George R., Scott, David W.
openaire   +2 more sources

evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

Journal of Statistical Software, 2018
evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated
Yang Hu, Carl Scarrott
doaj   +1 more source

Kernel density classification and boosting: an L2 sub analysis [PDF]

, 2005
Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification.
B.W. Silverman, C. C. Taylor, D. Michie, D.E. Wright, D.J. Hand, G. Ridgeway, G.R. Terrell, I.S. Abramson, J.D.F. Habbema, J.H. Friedman, J.H. Friedman, J.H. Friedman, M. Di Marzio, M. Di Marzio, M.C. Jones, M.C. Jones, M.C. Jones, M.P. Wand, P. Bühlmann, P. Hall, P. Hall, P. Hall, R.E. Shapire, T. Hastie, Y. Freund, Y. Freund   +25 more
core   +2 more sources