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Related papers: Isgur-Wise function in a QCD inspired potential mo…

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In recent study in Ref.[7] (arXiv: 2401.12525), we have introduced a method aimed at calculating the weak-field asymptotic deflection angle. This method offers an efficient computational approach that avoids the complexities of integration…

General Relativity and Quantum Cosmology · Physics 2024-02-02 Zonghai Li

In this paper we study algorithms to find a Gaussian approximation to a target measure defined on a Hilbert space of functions; the target measure itself is defined via its density with respect to a reference Gaussian measure. We employ the…

Numerical Analysis · Mathematics 2014-08-11 Frank J. Pinski , Gideon Simpson , Andrew M. Stuart , Hendrik Weber

The two-dimensional Hubbard model is studied using the variational quantum Monte Carlo technique with Gutzwiller-type variational wave functions. In addition to the simple one-site correlated Gutzwiller wave function, we use a form with…

Strongly Correlated Electrons · Physics 2007-05-23 A. Harju

Wentzel, Kramers, Brillouin (WKB) approximation for fractional systems is investigated in this paper using the fractional calculus. In the fractional case the wave function is constructed such that the phase factor is the same as the…

Mathematical Physics · Physics 2007-05-23 Eqab M. Rabei , Ibrahim M. A. Altarazi , Sami I. Muslih , Dumitru Baleanu

Pade-approximant methods are used to extract information about leading positive zeros or poles of QCD and SQCD beta-functions from the known terms of their perturbative series. For QCD, such methods are seen to corroborate the…

High Energy Physics - Phenomenology · Physics 2009-11-07 Victor Elias

Our previously-developed calculational method (the partial wave cutoff method) is employed to evaluate explicitly scalar one-loop effective actions in a class of radially symmetric background gauge fields. Our method proves to be…

High Energy Physics - Theory · Physics 2008-11-26 Gerald V. Dunne , Jin Hur , Choonkyu Lee , Hyunsoo Min

Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights. However, this approximation is without knowledge of the final application,…

Machine Learning · Statistics 2018-05-11 Adam D. Cobb , Stephen J. Roberts , Yarin Gal

The method for calculation of the correlation functions of the Ising-type systems with short-range interaction and with arbitrary value of spin is developed within cluster approximation. For the Ising model (spin $S^z=\pm1$) the expressions…

Condensed Matter · Physics 2007-05-23 R. R. Levitskii , S. I. Sorokov

Based on the Dirac approach we have developed the relativistic vision of the WKB method for centrally symmetrical potential with mixed Lorenz structure. We have obtained relativistic wavefunctions of light quark and the new rule of…

High Energy Physics - Phenomenology · Physics 2007-05-23 V. V. Rubish

The long standing problem on finding the height correlation function is studied by the inverse scattering problem. We propose a new method in the frame work of Kirchhoff theory which we call "path derivation of scattered wave (PDSW)" in…

Optics · Physics 2015-06-12 M. Zamani , S. M. Fazeli , M. Salami , S. Vasheghani Farahani , G. R. Jafari

Taking into account results of WKB-approximation, we derive exact quantum energies and wave functions of even and odd states in the one-dimensional Coulomb potential

Quantum Physics · Physics 2019-02-07 A. M. Ishkhanyan , V. P. Krainov

Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and has been widely used in applications, it suffers from some…

Machine Learning · Statistics 2022-07-28 Mingxuan Yi , Song Liu

The slope of the Isgur-Wise function at the normalization point, $\xi^{(1)}(1)$,is one of the basic parameters for the extraction of the $CKM$ matrix element $V_{cb}$ from exclusive semileptonic decay data. A method for measuring this…

High Energy Physics - Lattice · Physics 2009-10-28 U. Aglietti , V. Gimenez

Variational Bayesian inference is an important machine-learning tool that finds application from statistics to robotics. The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense…

Machine Learning · Computer Science 2022-09-27 Timothy D. Barfoot , Gabriele M. T. D'Eleuterio

Kernel approximation with exponentials is useful in many problems with convolution quadrature and particle interactions such as integral-differential equations, molecular dynamics and machine learning. This paper proposes a weighted…

Computational Physics · Physics 2025-05-07 Yuanshen Lin , Zhenli Xu , Yusu Zhang , Qi Zhou

Bayesian inference has many advantages for complex models, but standard Monte Carlo methods for summarizing the posterior can be computationally demanding, and it is attractive to consider optimization-based variational methods. Our work…

Computation · Statistics 2025-10-09 Aoxiang Chen , David J. Nott , Linda S. L. Tan

The space-like elastic form factor of heavy-light pseudoscalar mesons is investigated within a light-front constituent quark model in order to evaluate the Isgur-Wise form factor. The relativistic composition of the constituent quark spins…

High Energy Physics - Phenomenology · Physics 2009-10-28 S. Simula

Kullback--Leibler (KL) divergence is a fundamental measure of the dissimilarity between two probability distributions, but it can become unstable in high-dimensional settings due to its sensitivity to mismatches in distributional support.…

Information Theory · Computer Science 2025-02-03 Yifeng Peng , Dantong Li , Xinyi Li , Zhiding Liang , Yongshan Ding , Ying Wang

The paper presents the results of a study of the possibility of using the WKB approach to describe Inhomogeneous Travelling-Wave Accelerating Sections (ITWAS). This possibility not only simplifies the calculation, but also allows the use of…

Accelerator Physics · Physics 2021-03-22 M. I. Ayzatsky

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods…

Information Retrieval · Computer Science 2021-01-14 Chen Ma , Liheng Ma , Yingxue Zhang , Ruiming Tang , Xue Liu , Mark Coates