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We consider the problem of constructing nonparametric undirected graphical models for high-dimensional functional data. Most existing statistical methods in this context assume either a Gaussian distribution on the vertices or linear…

Statistics Theory · Mathematics 2021-03-22 Eftychia Solea , Holger Dette

Although symmetry methods and analysis are a necessary ingredient in every physicist's toolkit, rather less use has been made of combinatorial methods. One exception is in the realm of Statistical Physics, where the calculation of the…

Quantum Physics · Physics 2007-05-23 Allan I. Solomon , Pawel Blasiak , Gerard Duchamp , Andrzej Horzela , Karol A. Penson

A new approach to nonperturbative calculations in quantum electrodynamics is proposed. The approach is based on a regular iteration scheme for solution of Schwinger-Dyson equations for generating functional of Green functions. The approach…

High Energy Physics - Phenomenology · Physics 2008-11-26 V. E. Rochev

The AMAS group at the Paul Scherrer Institute developed an object oriented library for high performance simulation of high intensity ion beam transport with space charge. Such particle-in-cell (PIC) simulations require a method to generate…

Data Analysis, Statistics and Probability · Physics 2012-05-17 Christian Baumgarten

This paper proposes a new second-order symmetric algorithm for solving decoupled forward-backward stochastic differential equations. Inspired by the alternating direction implicit splitting method for partial differential equations, we…

Numerical Analysis · Mathematics 2026-01-16 Wenbo Wang , Guangyan Jia

We characterize the symmetric distributions that can be (approximately) generated by shallow Boolean circuits. More precisely, let $f\colon \{0,1\}^m \to \{0,1\}^n$ be a Boolean function where each output bit depends on at most $d$ input…

Computational Complexity · Computer Science 2025-11-19 Daniel M. Kane , Anthony Ostuni , Kewen Wu

Stock and flow diagrams are widely used in epidemiology to model the dynamics of populations. Although tools already exist for building these diagrams and simulating the systems they describe, we have created a new package called StockFlow,…

Logic in Computer Science · Computer Science 2023-08-01 John Baez , Xiaoyan Li , Sophie Libkind , Nathaniel D. Osgood , Evan Patterson

We devise a {\sl non--perturbative} method, called {\sl Parametric Perturbation Theory} (PPT), which is alternative to the ordinary perturbation theory. The method relies on a principle of simplicity for the observable solutions, which are…

High Energy Physics - Phenomenology · Physics 2007-05-23 Paolo Amore

A new numerical method is proposed for a 1-D inverse medium scattering problem with multi-frequency data. This method is based on the construction of a weighted cost functional. The weight is a Carleman Weight Function (CWF). In other…

Numerical Analysis · Mathematics 2017-03-24 Michael V. Klibanov , Aleksandr E. Kolesov , Lam Nguyen , Anders Sullivan

Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes, to the promise of serving as building blocks of quantitative descriptions of the electronic…

This work provides a survey of the general class of distributions generated from the mixture of the beta random variables. We provide an extensive review of the literature, concerning generating new distributions via the inverse CDF…

Methodology · Statistics 2020-05-12 Palash Sharma

We propose a new method for investigating the exclusion statistics of quasi-particles in Conformal Field Theory (CFT) spectra. The method leads to one-particle distribution functions, which generalize the Fermi-Dirac distribution. For the…

Statistical Mechanics · Physics 2011-08-17 K. Schoutens

We propose a probability distribution for multivariate binary random variables. The probability distribution is expressed as principal minors of the parameter matrix, which is a matrix analogous to the inverse covariance matrix in the…

Methodology · Statistics 2025-12-08 Takashi Arai

We present the derivation of an alternative representation of the real-time in-in formalism under a spatially homogeneous and time independent electric field. Because the system exhibits instability associated with pair production of…

High Energy Physics - Phenomenology · Physics 2026-02-27 Kenji Fukushima , Shuhei Minato

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as…

Machine Learning · Computer Science 2023-01-24 Arthur Leroy , Pierre Latouche , Benjamin Guedj , Servane Gey

We use functional methods to compute one-loop effects in Heavy Quark Effective Theory. The covariant derivative expansion technique facilitates the efficient extraction of matching coefficients and renormalization group evolution equations.…

High Energy Physics - Phenomenology · Physics 2020-07-15 Timothy Cohen , Marat Freytsis , Xiaochuan Lu

Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a number of important statistical quantities, particularly…

Machine Learning · Computer Science 2022-09-01 Chandramouli Shama Sastry , Andreas Lehrmann , Marcus Brubaker , Alexander Radovic

The dual-fermion approach provides a formally exact prescription for calculating properties of a correlated electron system in terms of a diagrammatic expansion around dynamical mean-field theory (DMFT). Most practical implementations,…

Strongly Correlated Electrons · Physics 2017-08-02 Jan Gukelberger , Evgeny Kozik , Hartmut Hafermann

We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on…

Machine Learning · Computer Science 2024-02-05 Hongyu Zhu , Stefan Klus , Tuhin Sahai

Bayesian parameter estimation on gravitational waves from compact binary coalescences (CBCs) typically requires millions of template waveform computations at different values of the parameters describing the binary. Sampling techniques such…

High Energy Astrophysical Phenomena · Physics 2014-09-05 Rory Smith , Chad Hanna , Ilya Mandel , Alberto Vecchio