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Let $d(n)$ be the number of divisors of $n$, let $$ \Delta(x) := \sum_{n\le x}d(n) - x(\log x + 2\gamma -1) $$ denote the error term in the classical Dirichlet divisor problem, and let $\zeta(s)$ denote the Riemann zeta-function. Several…

Number Theory · Mathematics 2016-11-16 Aleksandar Ivić

We generalize the na\"ive estimator of a Poisson regression model with measurement errors as discussed in Kukush et al. [1]. The explanatory variable is not always normally distributed as they assume. In this study, we assume that the…

Statistics Theory · Mathematics 2022-05-12 Kentarou Wada , Takeshi Kurosawa

We discuss generalizations of classical theta series, requiring only some basic properties of the classical setting. As it turns out, the existence of a generalized theta transformation formula implies that the series is defined over a…

Complex Variables · Mathematics 2022-09-20 Josef F. Dorfmeister , Sebastian Walcher

The paper gives a bound on the generalization error of the Gibbs algorithm, which recovers known data-independent bounds for the high temperature range and extends to the low-temperature range, where generalization depends critically on the…

Machine Learning · Computer Science 2025-04-07 Andreas Maurer

Let $(\mathbb{R}_{\alpha ,\beta ,\gamma }(z))_{m}(z)=z+\sum_{n=1}^{m}A_{n}z^{n+1}$ be the sequence of partial sums of the normalized Rabotnov functions $\mathbb{R}_{\alpha ,\beta ,\gamma }(z)=z+\sum_{n=1}^{\infty }A_{n}z^{n+1}$ where…

Complex Variables · Mathematics 2023-09-06 Basem Aref Frasin

A method for describing the quantum kink states in the semi-classical limit of several (1+1)-dimensional field theoretical models is developed. We use the generalized zeta function regularization method to compute the one-loop quantum…

High Energy Physics - Theory · Physics 2015-06-26 A. Alonso Izquierdo , W. Garcia Fuertes , M. A. Gonzalez Leon , J. Mateos Guilarte

Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test…

Methodology · Statistics 2021-05-18 David Issa Mattos , Jan Bosch , Helena Holmström Olsson

General formulas as well as leading asymptotics for corrections to Z- and W-boson parameters are derived. Experimental data fits in Standard Model and its extensions are discussed and results are presented.

High Energy Physics - Phenomenology · Physics 2008-12-16 M. I. Vysotsky

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as:…

Data Analysis, Statistics and Probability · Physics 2009-11-10 G. D'Agostini

We consider the problem of reducing the dimensions of parameters and data in non-Gaussian Bayesian inference problems. Our goal is to identify an "informed" subspace of the parameters and an "informative" subspace of the data so that a…

Computation · Statistics 2022-07-19 Ricardo Baptista , Youssef Marzouk , Olivier Zahm

In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. These inference-hyperparameters can't be estimated jointly with the other parameters, from the data, by giving them a prior.…

Methodology · Statistics 2026-05-18 Jeong Eun Lee , Sitong Liu , Geoff K. Nicholls

Assume the Riemann hypothesis throughout. We obtain some new estimates for the size of the set of large values of the error term in the prime number theorem. Our argument is based on an analysis of the behavior of zeros of the Riemann zeta…

Number Theory · Mathematics 2023-01-24 Bryce Kerr

In this short note, we briefly comment on the analytical bounds that must be imposed on the parameter space of the Rezzolla-Zhidenko (RZ) metric-parametrization approach introduced in Ref. [1]. We hope this will clarify some of the…

General Relativity and Quantum Cosmology · Physics 2022-06-08 Prashant Kocherlakota , Luciano Rezzolla

Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given…

Machine Learning · Computer Science 2016-03-11 Kevin R. Moon , Veronique Delouille , Alfred O. Hero

Accurate identification of parameters of load models is essential in power system computations, including simulation, prediction, and stability and reliability analysis. Conventional point estimation based composite load modeling approaches…

Systems and Control · Computer Science 2019-03-27 Chang Fu , Zhe Yu , Di Shi , Haifeng Li , Caisheng Wang , Zhiwei Wang , Jie Li

A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an…

Methodology · Statistics 2019-08-29 Toru Imai

Necessary and sufficient criteria for metric subregularity (or calmness) of set-valued mappings between general metric or Banach spaces are treated in the framework of the theory of error bounds for a special family of extended real-valued…

Optimization and Control · Mathematics 2015-01-20 Alexander Y. Kruger

A global network of laser interferometric gravitational wave detectors is projected to be in operation by around the turn of the century. Here, the noisy output of a single instrument is examined. A gravitational wave is assumed to have…

General Relativity and Quantum Cosmology · Physics 2009-10-30 David Nicholson , Alberto Vecchio

This paper provides new sufficient and necessary conditions for the frame property of generalized translation-invariant systems. The conditions are formulated in the Fourier domain and consists of estimates involving the upper and lower…

Functional Analysis · Mathematics 2022-01-20 Jakob Lemvig , Jordy Timo van Velthoven

In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature, many attempts have been made to model trust with aid of…

Cryptography and Security · Computer Science 2020-01-14 Bin Liu