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Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature…

Machine Learning · Computer Science 2013-05-07 Ji Won Yoon , Nial Friel

Algorithms for numerical computation of symmetric elliptic integrals of all three kinds are improved in several ways and extended to complex values of the variables (with some restrictions in the case of the integral of the third kind).…

Classical Analysis and ODEs · Mathematics 2015-06-26 Bille C. Carlson

Discrete Markov random fields form a natural class of models to represent images and spatial data sets. The use of such models is, however, hampered by a computationally intractable normalising constant. This makes parameter estimation and…

Computation · Statistics 2015-05-25 Haakon Michael Austad , Håkon Tjelmeland

In this work we present a method, based on the use of Bernstein polynomials, for the numerical resolution of some boundary values problems. The computations have not need of particular approximations of derivatives, such as finite…

Numerical Analysis · Mathematics 2025-10-20 Gianluca Argentini

We consider finite element approximations of unique continuation problems subject to elliptic equations in the case where the normal derivative of the exact solution is known to reside in some finite dimensional space. To give quantitative…

Numerical Analysis · Mathematics 2025-03-13 Erik Burman , Lauri Oksanen , Ziyao Zhao

The molecule solution of an equation related to the lattice Boussinesq equation is derived with the help of determinantal identities. It is shown that this equation can for certain sequences be used as a numerical convergence acceleration…

Numerical Analysis · Mathematics 2011-05-17 Yi He , Xing-Biao Hu , Jian-Qing Sun , Ernst Joachim Weniger

It is important to estimate the errors of probabilistic inference algorithms. Existing diagnostics for Markov chain Monte Carlo methods assume inference is asymptotically exact, and are not appropriate for approximate methods like…

Machine Learning · Computer Science 2021-03-02 Justin Domke

Estimating the marginal likelihoods is an essential feature of model selection in the Bayesian context. It is especially crucial to have good estimates when assessing the number of planets orbiting stars when the models explain the noisy…

Earth and Planetary Astrophysics · Physics 2015-06-03 Mikko Tuomi , Hugh R. A. Jones

We introduce polynomial couplings, a generalization of probabilistic couplings, to develop an algorithm for the computation of equivalence relations which can be interpreted as a lifting of probabilistic bisimulation to polynomial…

Logic in Computer Science · Computer Science 2021-04-28 Giorgio Bacci , Giovanni Bacci , Kim G. Larsen , Mirco Tribastone , Max Tschaikowski , Andrea Vandin

We present a simple algebraic method for the analytic continuation of harmonic sums with integer real or purely imaginary indices near negative and positive integers. We provide a MATHEMATICA code for exact expansion of harmonic sums in a…

High Energy Physics - Theory · Physics 2023-06-07 V. N. Velizhanin

We consider hard-potential cutoff multi-species Boltzmann operators modeling microscopic binary elastic collisions and bimolecular reversible chemical reactions inside a gaseous mixture. We prove that the spectral gap estimate derived for…

Analysis of PDEs · Mathematics 2025-11-27 Andrea Bondesan , Bao Quoc Tang

Physical models often contain unknown functions and relations. In order to gain more insights into the nature of physical processes, these unknown functions have to be identified or reconstructed. Mathematically, we can formulate this…

Optimization and Control · Mathematics 2026-05-19 Jan Bartsch , Ahmed A. Barakat , Simon Buchwald , Gabriele Ciaramella , Stefan Volkwein , Eva M. Weig

The article introduces a new algorithm for solving a class ofequilibrium problems involving strongly pseudomonotone bifunctions with Lipschitz-type condition. We describe how to incorporate the proximal-like regularized technique with…

Optimization and Control · Mathematics 2018-04-26 Dang Van Hieu

This work addresses the problem of (global) maximal regularity for quasilinear evolution equations with sublinear gradient growth and right-hand side in Lebesgue spaces, complemented with Neumann boundary conditions. The proof relies on a…

Analysis of PDEs · Mathematics 2024-04-09 Alessandro Goffi , Tommaso Leonori

This work is devoted to the obtaining of a new numerical scheme based in quadrature formulas for the Lebesgue-Stieltjes integral for the approximation of Stieltjes ordinary differential equations. This novel method allows us to numerically…

Numerical Analysis · Mathematics 2020-02-20 Francisco J. Fernández , F. Adrián F. Tojo

We present a novel Krylov subspace method for approximating $L_f(A, E) \vc{b}$, the matrix-vector product of the Fr\'echet derivative $L_f(A, E)$ of a large-scale matrix function $f(A)$ in direction $E$, a task that arises naturally in the…

Numerical Analysis · Mathematics 2026-01-30 Daniel Kressner , Peter Oehme

This paper is devoted to study the approximation properties and rate of approximation of the Szasz-Mirakjan-Kantrovich-Stancu type polynomials generated by the Dunkl generalization of the exponential function with respect to q -calculus. We…

Classical Analysis and ODEs · Mathematics 2016-03-21 M. Mursaleen , Md. Nasiruzzaman

We propose a discrete lattice version of the Fokker-Planck kinetic equation along lines similar to the Lattice-Boltzmann scheme. Our work extends an earlier one-dimensional formulation to arbitrary spatial dimension $D$. A generalized…

Statistical Mechanics · Physics 2009-11-11 Daniele Moroni , Benjamin Rotenberg , Jean-Pierre Hansen , Sauro Succi , Simone Melchionna

We seek random versions of some classical theorems on complex approximation by polynomials and rational functions, as well as investigate properties of random compact sets in connection to complex approximation.

Complex Variables · Mathematics 2017-09-26 Simon St-Amant , Jérémie Turcotte

We consider the problem of discretizing evolution operators of linear delay equations with the aim of approximating their spectra, which is useful in investigating the stability properties of (nonlinear) equations via the principle of…

Numerical Analysis · Mathematics 2026-01-01 Alessia andò , Giusy Bosco , Dimitri Breda , Davide Liessi
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