Results 1 to 10 of about 19,193 (160)
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
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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
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Near-native protein loop sampling using nonparametric density estimation accommodating sparcity. [PDF]
PLoS Computational Biology, 2011 Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of ...
Hyun Joo +7 more
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A nonparametric statistical method for deconvolving densities in the analysis of proteomic data [PDF]
BMC BioinformaticsBackground Genomic or proteomic data are frequently affected by noise or are convolutions of different biological signals. This is, e.g., particularly relevant in skin aging research, in which intrinsic aging, driven by genetic factors, and extrinsic ...
Akin Anarat +2 more
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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
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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
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Sequential nonparametric density estimation [PDF]
IEEE Transactions on Information Theory, 1975 Using kernel estimates of the Parzen type, a naive sequential nonparametric density estimation procedure is developed. The asymptotic distribution structure of the stopping variable is examined. The stopping variable is shown to have finite moments of ail order and is shown to be dosed.
H. I. Davies, Edward J. Wegman
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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
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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
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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
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