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Quick Selecting Kernel Blur Coefficients to Estimate Probability Density for Independent Random Variables
AbstractA technique to select fast blur coefficients of kernel functions is proposed to proceed with a nonparametric estimation of the probability...
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Analysis of optimization methods for nonparametric estimation of probability density in large samples
The article proposes a procedure for selecting the blur factor of kernel functions for nonparametric density estimation of a one-dimensional random...
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Analysis of the Ratio of the Standard Deviations of the Kernel Estimate of the Probability Density with Independent and Dependent Random Variables
The influence of information about the dependence of random variables on the approximation properties of a nonparametric estimate of the probability...
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Fast Selection of Bandwidths for Nonparametric Estimation of the Probability Density of a Two-Dimensional Random Variable with Dependent Components
AbstractA method for fast selection of bandwidths of kernel functions in the nonparametric estimation of a two-dimensional random variable with...
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Estimation of a Nonlinear Functional of the Probability Density of a Three-Dimensional Random Variable to Improve the Computational Efficiency of Nonparametric Decision Rules
AbstractWe propose a method for estimating a nonlinear functional of the probability density of a three-dimensional random variable. This technique...
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Modified Algorithm for Rapid Choice of Spread Coefficients for Kernel Estimates of Multidimensional Probability Densities
An original method for rapid choice of the spread coefficients of the kernel functions in nonparametric estimates of multidimensional...
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Dependencies Between Histogram Parameters and the Kernel Estimate of the Probability Density of a Multidimensional Random Variable
The dependencies between the sampling intervals of the domain of values of a multidimensional random variable and the blur coefficients of the kernel...
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Dependence Between Histogram Parameters and the Kernel Estimate of a Unimodal Probability Density
The dependence between the sampling interval of the domain of values of a one-dimensional random variable and the blur coefficient of the kernel...
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Estimation of a Nonlinear Functional of Probability Density when Optimizing Nonparametric Decision Functions
We propose a method for estimating the nonlinear functional of the probability density of a two-dimensional random variable. The technique applies to...
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A Technique for Rapid Selection of Blur Coefficients for Kernel Functions in Nonparametric Regression
A technique for rapid selection of blur coefficients for kernel functions in nonparametric regression is proposed. This technique increases the...
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An Unconventional Technique for Choosing the Kernel Function Blur Coefficients in Nonparametric Regression
The traditional method for choosing the kernel function blur coefficients in nonparametric regression is based on minimizing the root mean square...
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Modified Fast Algorithm for the Bandwidth Selection of the Kernel Density Estimation
AbstractA modification of the fast algorithm for the bandwidth selection of kernel functions in a nonparametric probability density estimate of the...
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A Mixed-Norm Estimate of the Two-Particle Reduced Density Matrix of Many-Body Schrödinger Dynamics for Deriving the Vlasov Equation
We re-examine the combined semi-classical and mean-field limit in the N -body fermionic Schrödinger equation with pure state initial data using the...
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Quantum kernel estimation-based quantum support vector regression
Quantum machine learning endeavors to exploit quantum mechanical effects like superposition, entanglement, and interference to enhance the...
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Performance of quantum kernel on initial learning process
For many manufacturing companies, the production line is very important. In recent years, the number of small-quantity, high-mix products have been...
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Lattice calculation of the intrinsic soft function and the Collins-Soper kernel
We calculate the soft function using lattice QCD in the framework of large momentum effective theory incorporating the one-loop perturbative...
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Stabilizing complex Langevin for real-time gauge theories with an anisotropic kernel
The complex Langevin (CL) method is a promising approach to overcome the sign problem that occurs in real-time formulations of quantum field...
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Selection of the Blur Coefficient for Probability Density Kernel Estimates Under Conditions of Large Samples
A fast algorithm is proposed for choosing the blur factors of kernel functions of a non-parametric probability density estimate under conditions of...
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Neutron elastic scattering kernel for Monte Carlo next-event estimators in Tripoli-4®
This paper presents the implementation and the numerical validation of the exponential track length estimator ( e TLE) for neutron transport in...
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Machine-learned exclusion limits without binning
Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the...
