Results 11 to 20 of about 18,318 (97)
Adaptive Nonparametric Density Estimation with B-Spline Bases
Mathematics, 2023 Learning density estimation is important in probabilistic modeling and reasoning with uncertainty. Since B-spline basis functions are piecewise polynomials with local support, density estimation with B-splines shows its advantages when intensive ...
Yanchun Zhao +3 more
doaj +1 more source
High throughput nonparametric probability density estimation. [PDF]
PLoS ONE, 2018 In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity.
Jenny Farmer, Donald Jacobs
doaj +1 more source
Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
Algorithms, 2023 We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate a probability density function (PDF) of a single variable.
Zach D. Merino +2 more
doaj +1 more source
MATLAB tool for probability density assessment and nonparametric estimation
SoftwareX, 2022 A MATLAB function is presented for nonparametric probability density estimation, based on an iterative method that employs the principle of maximum entropy and characteristic properties of single order statistics.
Jenny Farmer, Donald J. Jacobs
doaj +1 more source
Energy Reports, 2022 In today’s increasingly serious world energy crisis, Renewable energy such as wind energy has gradually penetrated into life. Aiming at the uncertainty of wind power and the need of a mass of sample data in nonparametric kernel density estimation, a wind
Kai Zhang +6 more
doaj +1 more source
Research of nonparametric density estimation algorithms by applying clustering methods
Lietuvos Matematikos Rinkinys, 2023 One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster ...
Rasa Šmidtaitė, Tomas Ruzgas
doaj +3 more sources
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
Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
doaj +1 more source
Robust Localization Method Based on Non-Parametric Probability Density Estimation
IEEE Access, 2023 This paper presents robust localization techniques that calculate location using distance observations. In enclosed and heavily populated urban environments, the positive measurement bias introduced by a non-line-of-sight signal can have a considerable ...
Chee-Hyun Park, Joon-Hyuk Chang
doaj +1 more source
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Econometrics, 2016 This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density.
Xibin Zhang +2 more
doaj +1 more source