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We consider an approximation scheme for multivariate information assuming that synergistic information only appearing in higher order joint distributions is suppressed, which may hold in large classes of systems. Our approximation scheme…

Methodology · Statistics 2020-09-03 Masahiro Takimoto

We propose a chiral quark model including the $\omega$ and $\rho$ meson contributions in addition to the $\pi$ and $\sigma$ meson contributions. We show that the masses of the ground state baryons such as the nucleon, $\Lambda_c$ and…

High Energy Physics - Phenomenology · Physics 2023-06-28 Bing-Ran He , Masayasu Harada , Bing-Song Zou

We use the concept of barrier-based smoothing approximations introduced in [ C. B. Chua and Z. Li, A barrier-based smoothing proximal point algorithm for NCPs over closed convex cones, SIOPT 23(2), 2010] to extend the non-interior…

Optimization and Control · Mathematics 2020-03-06 Le Thi Khanh Hien , Chek Beng Chua

We study robust high-dimensional sparse regression under finite-variance heavy-tailed noise, epsilon-contamination, and alpha-mixing dependence via two subsampling estimators: Adaptive Importance Sampling (AIS) and Stratified Sub-sampling…

Statistics Theory · Mathematics 2026-03-11 Prateek Mittal , Joohi Chauhan

In this paper, we introduce the convergence analysis of the fixed pivot technique given by S.Kumar and Ramkrishna \cite{Kumar:1996-1} for the nonlinear aggregation population balance equations which are of substantial interest in many areas…

Numerical Analysis · Mathematics 2013-03-26 Ankik Kumar Giri , Erika Hausenblas

This work is divided in two parts. In the first three sections we review the meson physics phenomenology, highlighting the history of the pseudoscalar multiplet mass spectra research. Then we propose a new approach for the mass mixing…

High Energy Physics - Phenomenology · Physics 2025-11-17 R. L. P. G. Amaral , V. E. R. Lemes , O. S. Ventura , L. C. Q. Vilar

We study Bayesian estimation of mixture models and argue in favor of fitting the marginal posterior distribution over component assignments directly, rather than Gibbs sampling from the joint posterior on components and parameters as is…

Computation · Statistics 2025-11-03 M. E. J. Newman

This paper adopts a Bayesian nonparametric mixture model where the mixing distribution belongs to the wide class of normalized homogeneous completely random measures. We propose a truncation method for the mixing distribution by discarding…

Statistics Theory · Mathematics 2015-07-17 Raffaele Argiento , Ilaria Bianchini , Alessandra Guglielmi

We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of convexity-concavity and relative smoothness of the payoff…

Optimization and Control · Mathematics 2026-05-08 Razvan-Andrei Lascu , Mateusz B. Majka , Łukasz Szpruch

We provide a model-independent determination of the quantity B_0(m_d-m_u). Our approach rests only on chiral symmetry and data from the decay of the eta into three neutral pions. Since the low-energy prediction at next-to-leading order…

High Energy Physics - Phenomenology · Physics 2008-11-26 A. Deandrea , A. Nehme , P. Talavera

In this work we obtain mixing (and in some cases sharp mixing rates) for a reasonable large class of invertible systems preserving an infinite measure. The examples considered here are the invertible analogue of both Markov and non Markov…

Dynamical Systems · Mathematics 2014-11-24 Carlangelo Liverani , Dalia Terhesiu

The design of informatively rich input signals is essential for accurate system identification, yet classical Fisher-information-based methods are inherently local and often inadequate in the presence of significant model uncertainty and…

Statistics Theory · Mathematics 2025-12-15 Piotr Bania , Anna Wójcik

Mixture models are widely used in Bayesian statistics and machine learning, in particular in computational biology, natural language processing and many other fields. Variational inference, a technique for approximating intractable…

Statistics Theory · Mathematics 2020-08-03 Badr-Eddine Chérief-Abdellatif , Pierre Alquier

Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point…

Optimization and Control · Mathematics 2026-04-09 Siddharth Chandak

Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via…

Computation · Statistics 2021-05-17 Ben Seiyon Lee , Murali Haran

Rational approximation appears in many contexts throughout science and engineering, playing a central role in linear systems theory, special function approximation, and many others. There are many existing methods for solving the rational…

Numerical Analysis · Mathematics 2018-12-03 Jeffrey M. Hokanson , Caleb C. Magruder

Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions. If these datasets can be efficiently exploited, they can…

Methodology · Statistics 2015-12-08 Matthias Katzfuss

The meson masses and mixing angles have been calculated for the scalar and pseudoscalar sector in the framework of the generalized 2+1 flavor Polyakov loop augmented quark meson linear sigma model. We have given the results for two…

High Energy Physics - Phenomenology · Physics 2010-04-29 Uma Shankar Gupta , Vivek Kumar Tiwari

We propose a supersymmetric A4 x SU(5) model of quasi-degenerate neutrinos which predicts the effective neutrino mass m_ee relevant for neutrinoless double beta decay to be proportional to the neutrino mass scale, thereby allowing its…

High Energy Physics - Phenomenology · Physics 2011-04-01 Stefan Antusch , Stephen F. King , Martin Spinrath

Optimizing Reconfigurable Intelligent Surfaces (RIS) is a high-dimensional combinatorial challenge. Current quantum algorithms often simplify this problem by ignoring physical constraints like mutual coupling, which significantly degrades…

Computational Engineering, Finance, and Science · Computer Science 2026-05-08 Marco Pasquale , Erik M. Åsgrim , Stefano Markidis , Oscar Quevedo-Teruel
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