English
Related papers

Related papers: The dependence of observables on action parameters

200 papers

We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation…

Machine Learning · Computer Science 2012-06-22 M. Pawan Kumar , Ben Packer , Daphne Koller

We present a general class of unbiased improved estimators for physical observables in lattice gauge theory computations which significantly reduces statistical errors at modest computational cost. The error reduction techniques, referred…

High Energy Physics - Lattice · Physics 2013-11-13 Thomas Blum , Taku Izubuchi , Eigo Shintani

We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are…

Computational Finance · Quantitative Finance 2018-03-21 Gábor Petneházi , József Gáll

We investigate the connections between some simple Maier-Saupe lattice models, with a discrete choice of orientations of the microscopic directors, and a recent proposal of a two-tensor formalism to describe the phase diagrams of nematic…

Soft Condensed Matter · Physics 2016-09-21 E. S. Nascimento , A. P. Vieira , S. R. Salinas

We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural networks. This includes histogram reweighting and finite-size…

High Energy Physics - Lattice · Physics 2021-09-20 Gert Aarts , Dimitrios Bachtis , Biagio Lucini

Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case. The limiting performance of such estimators depends on the…

Machine Learning · Computer Science 2022-03-22 Nhat Ho , Koulik Khamaru , Raaz Dwivedi , Martin J. Wainwright , Michael I. Jordan , Bin Yu

The problem of fitting experimental data to a given model function $f(t; p_1,p_2,\dots,p_N)$ is conventionally solved numerically by methods such as that of Levenberg-Marquardt, which are based on approximating the Chi-squared measure of…

Optimization and Control · Mathematics 2017-03-14 Alberto Herrera-Gomez , R. Michael Porter

The treatment of hypercubic lattice artifacts is essential for the calculation of non-perturbative renormalization constants of RI-MOM schemes. It has been shown that for the RI'-MOM scheme a large part of these artifacts can be calculated…

High Energy Physics - Lattice · Physics 2015-01-27 Jakob Simeth , Andre Sternbeck , Meinulf Gockeler , Holger Perlt , Arwed Schiller

Generally, natural scientific problems are so complicated that one has to establish some effective perturbation or nonperturbation theories with respect to some associated ideal models. In this Letter, a new theory that combines…

Computational Physics · Physics 2015-05-13 Yuan Gao , S. Y. Lou

This work presents a tensorial approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian structure. By expressing parameter-varying operators with…

Numerical Analysis · Mathematics 2025-05-14 Arjun Vijaywargiya , Shane A. McQuarrie , Anthony Gruber

We revisit the old problem of which is the best single particle basis to express a Hubbard-like lattice model. A rigorous variational solution of this problem leads to equations in which the answer depends in a self-consistent manner on the…

Strongly Correlated Electrons · Physics 2017-01-18 Valentina Brosco , Zu-Jian Ying , José Lorenzana

The Taylor expansion is a widely used and powerful tool in all branches of Mathematics, both pure and applied. In Probability and Mathematical Statistics, however, a stronger version of Taylor's classical theorem is often needed, but only…

Other Statistics · Statistics 2023-05-09 Gianluca Viggiano

Taylor's law, also known as fluctuation scaling in physics and the power-law variance function in statistics, is an empirical pattern widely observed across fields including ecology, physics, finance, and epidemiology. It states that the…

Statistics Theory · Mathematics 2025-10-13 Pok Him Cheng , Joel E. Cohen , Hok Kan Ling , Sheung Chi Phillip Yam

We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD and emphasize the need for new methods to deal with finite-density and real-time evolution. We show that these…

High Energy Physics - Lattice · Physics 2022-09-21 Yannick Meurice , Ryo Sakai , Judah Unmuth-Yockey

We present a new method to calculate formation of cosmological structure in the Newtonian limit. The method is based on Lagrangian perturbation theory plus two key theoretical extensions. One advance involves identifying and fixing a…

Cosmology and Nongalactic Astrophysics · Physics 2014-12-15 Sharvari Nadkarni-Ghosh , David F. Chernoff

We study the eigenvalue spectrum of different lattice Dirac operators (staggered, fixed point, overlap) and discuss their dependence on the topological sectors. Although the model is 2D (the Schwinger model with massless fermions) our…

High Energy Physics - Lattice · Physics 2015-06-25 F. Farchioni , I. Hip , C. B. Lang

Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers that are performatively stable, i.e. optimal for the data distribution they induce. Standard…

Machine Learning · Computer Science 2025-02-07 Mehrnaz Mofakhami , Ioannis Mitliagkas , Gauthier Gidel

We consider the problem of adaptive observer design in the settings when the system is allowed to be nonlinear in the parameters, and furthermore they are to satisfy additional feasibility constraints. A solution to the problem is proposed…

Optimization and Control · Mathematics 2014-12-18 I. Yu. Tyukin , P. A. Rogachev , H. Nijmeijer

We consider the problem of asymptotic reconstruction of the state and parameter values in systems of ordinary differential equations. A solution to this problem is proposed for a class of systems of which the unknowns are allowed to be…

Optimization and Control · Mathematics 2015-03-13 Ivan Y. Tyukin , Erik Steur , Henk Nijmeijer , Cees van Leeuwen

We propose a technique for the design and analysis of adaptation algorithms in dynamical systems. The technique applies both to systems with conventional Lyapunov-stable target dynamics and to ones of which the desired dynamics around the…

Optimization and Control · Mathematics 2007-05-23 Tyukin Ivan , Danil Prokhorov , Cees van Leeuwen
‹ Prev 1 3 4 5 6 7 10 Next ›