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Related papers: Minimum-entropy constraints on galactic potentials

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We introduce the Minimum Entropy Method, a simple statistical technique for constraining the Milky Way gravitational potential and simultaneously testing different gravity theories directly from 6D phase-space surveys and without adopting…

Astrophysics of Galaxies · Physics 2015-06-11 Jorge Peñarrubia , Sergey E. Koposov , Matthew G. Walker

We introduce the emPDF (Empirical Distribution Function), a novel dynamical modeling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state.…

Astrophysics of Galaxies · Physics 2025-02-27 Zhaozhou Li , Jiaxin Han , Wenting Wang , Yong-Zhong Qian , Qingyang Li , Yipeng Jing , Ting S. Li

Problems of probabilistic inference and decision making under uncertainty commonly involve continuous random variables. Often these are discretized to a few points, to simplify assessments and computations. An alternative approximation is…

Artificial Intelligence · Computer Science 2013-03-08 William B. Poland , Ross D. Shachter

We develop two general methods to infer the gravitational potential of a system using steady-state tracers, i.e., tracers with a time-independent phase-space distribution. Combined with the phase-space continuity equation, the time…

Astrophysics of Galaxies · Physics 2016-01-13 Jiaxin Han , Wenting Wang , Shaun Cole , Carlos S. Frenk

A method is proposed for constraining the Galactic gravitational potential from high precision observations of the phase space coordinates of a system of relaxed tracers. The method relies on an "ergodic" assumption that the observations…

Astrophysics of Galaxies · Physics 2014-11-20 Adi Nusser

We examine the minimization of information entropy for measures on the phase space of bounded domains, subject to constraints that are averages of grand canonical distributions. We describe the set of all such constraints and show that it…

Mathematical Physics · Physics 2019-10-02 Stamatis Dostoglou , Alexander Hughes , Jianfei Xue

This article is devoted to the study of the dynamical behavior of a collisionless kinetic gas in d=1,2,3 space dimensions which is trapped in a rotationally symmetric potential well. Although at the microscopic level the trajectories of…

General Relativity and Quantum Cosmology · Physics 2020-08-19 Paola Rioseco , Olivier Sarbach

We show that the principle of maximum entropy, a variational method appearing in statistical inference, statistical physics, and the analysis of stochastic dynamical systems, admits a geometric description from gauge theory. Using the…

Mathematical Physics · Physics 2023-01-05 Dalton A R Sakthivadivel

A unified formulation of the density functional theory is constructed on the foundations of entropic inference in both the classical and the quantum regimes. The theory is introduced as an application of entropic inference for inhomogeneous…

Statistical Mechanics · Physics 2021-12-20 Ahmad Yousefi

A polymer chain pinned in space exerts a fluctuating force on the pin point in thermal equilibrium. The average of such fluctuating force is well understood from statistical mechanics as an entropic force, but little is known about the…

Statistical Mechanics · Physics 2015-07-22 James T. Waters , Harold D. Kim

In its continuous version, the entropy functional measuring the information content of a given probability density may be plagued by a "measure" problem that results from improper weighting of phase space. This issue is addressed…

Statistical Mechanics · Physics 2011-06-08 P. Maynar , E. Trizac

We propose a method to derive the stationary size distributions of a system, and the degree distributions of networks, using maximisation of the Gibbs-Shannon entropy. We apply this to a preferential attachment-type algorithm for systems of…

Physics and Society · Physics 2020-03-17 Cornelia Metzig , Caroline Colijn

Many Imitation and Reinforcement Learning approaches rely on the availability of expert-generated demonstrations for learning policies or value functions from data. Obtaining a reliable distribution of trajectories from motion planners is…

Robotics · Computer Science 2021-07-13 Alexander Lambert , Byron Boots

Gaussian mixture distributions are commonly employed to represent general probability distributions. Despite the importance of using Gaussian mixtures for uncertainty estimation, the entropy of a Gaussian mixture cannot be calculated…

Machine Learning · Statistics 2025-01-23 Takashi Furuya , Hiroyuki Kusumoto , Koichi Taniguchi , Naoya Kanno , Kazuma Suetake

Mobility entropy is proposed to measure predictability of human movements, based on which, the upper and lower bound of prediction accuracy is deduced, but corresponding mathematical expressions of prediction accuracy keeps yet open. In…

Social and Information Networks · Computer Science 2019-01-29 Lu Liu , Wuyang Zhou , Sihai Zhang , Wei Cai

Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formulation on the image space for which the minimizers are…

Statistics Theory · Mathematics 2019-12-05 Valentin De Bortoli , Agnes Desolneux , Alain Durmus , Bruno Galerne , Arthur Leclaire

The entropy accumulation theorem, and its subsequent generalized version, is a powerful tool in the security analysis of many device-dependent and device-independent cryptography protocols. However, it has the drawback that the finite-size…

Quantum Physics · Physics 2025-12-22 Amir Arqand , Thomas A. Hahn , Ernest Y. -Z. Tan

The entanglement entropy of a subsystem of a quantum system is expressed, in the replica approach, through analytic continuation with respect to n of the trace of the n-th power of the reduced density matrix. This trace can be thought of as…

High Energy Physics - Theory · Physics 2008-12-18 Michele Caraglio , Ferdinando Gliozzi

This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional sampling process of generative…

Machine Learning · Computer Science 2024-11-05 Jacob K Christopher , Stephen Baek , Ferdinando Fioretto

The estimation of categorical distributions under marginal constraints summarizing some sample from a population in the most-generalizable way is key for many machine-learning and data-driven approaches. We provide a parameter-agnostic…

High Energy Physics - Theory · Physics 2023-11-17 Orestis Loukas , Ho Ryun Chung
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