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We study a general framework of distributional computational graphs: computational graphs whose inputs are probability distributions rather than point values. We analyze the discretization error that arises when these graphs are evaluated…

Machine Learning · Statistics 2026-02-13 Olof Hallqvist Elias , Michael Selby , Phillip Stanley-Marbell

Riemann's hypothesis, formulated in 1859, concerns the location of the zeros of Riemann's Zeta function. The history of the Riemann hypothesis is well known. In 1859, the German mathematician B. Riemann presented a paper to the Berlin…

General Mathematics · Mathematics 2020-12-08 Jean Max Coranson Beaudu

We construct a Beurling generalized number system satisfying the Riemann hypothesis and whose integer counting function displays extremal oscillation in the following sense. The prime counting function of this number system satisfies…

Number Theory · Mathematics 2020-10-16 Frederik Broucke , Gregory Debruyne , Jasson Vindas

Online learning methods yield sequential regret bounds under minimal assumptions and provide in-expectation risk bounds for statistical learning. However, despite the apparent advantage of online guarantees over their statistical…

Machine Learning · Computer Science 2023-08-16 Dirk van der Hoeven , Nikita Zhivotovskiy , Nicolò Cesa-Bianchi

We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For the rejection sampling version of ABC, it is known that…

Computation · Statistics 2016-02-18 Luke Bornn , Natesh Pillai , Aaron Smith , Dawn Woodard

We address the problem of detecting deviations of binary sequence from randomness,which is very important for random number (RNG) and pseudorandom number generators (PRNG). Namely, we consider a null hypothesis $H_0$ that a given bit…

Information Theory · Computer Science 2007-07-13 B. Ya. Ryabko , V. A. Monarev

In this paper we study the convergence of online gradient descent algorithms in reproducing kernel Hilbert spaces (RKHSs) without regularization. We establish a sufficient condition and a necessary condition for the convergence of excess…

Machine Learning · Computer Science 2017-08-11 Yunwen Lei , Lei Shi , Zheng-Chu Guo

The assumption of real-number arithmetic, which is at the basis of conventional geometric algorithms, has been seriously challenged in recent years, since digital computers do not exhibit such capability. A geometric predicate usually…

Computational Geometry · Computer Science 2007-05-23 Olivier Devillers , Franco P. Preparata

This note describes the results of some tests of the ABC-PRC algorithm of Sissons et al. (PNAS, 2007), and demonstrates with a toy example that the method does not converge on the true posterior distribution.

Computation · Statistics 2008-05-21 Mark A. Beaumont

The compensated quotient-difference (Compqd) algorithm is proposed along with some applications. The main motivation is based on the fact that the standard quotient-difference (qd) algorithm can be numerically unstable. The Compqd algorithm…

Numerical Analysis · Mathematics 2017-02-20 Peibing Du , Roberto Barrio , Hao Jiang , Lizhi Cheng

We point out that $R \equiv Br(b\rightarrow d \gamma)/Br(b\rightarrow s \gamma)$ is a sensitive probe of possible violation of CKM unitarity. We compute $R$ in a minimal extension of the Standard Model containing an additional isosinglet…

High Energy Physics - Phenomenology · Physics 2010-11-01 Gautam Bhattacharyya , Gustavo C. Branco , Debajyoti Choudhury

Numerical integrations in celestial mechanics often involve the repeated computation of a rotation with a constant angle. A direct evaluation of these rotations yields a linear drift of the distance to the origin. This is due to roundoff in…

Numerical Analysis · Mathematics 2025-10-20 M. Henon , J-M. Petit

We present a procedure for handling asymmetric errors. Many results in particle physics are presented as values with different positive and negative errors, and there is no consistent procedure for handling them. We consider the difference…

Methodology · Statistics 2025-08-05 Roger Barlow , Alessandra Brazzale , Igor Volobouev

The finite Dirichlet series from the title are defined by the condition that they vanish at as many initial zeroes of the zeta function as possible. It turned out that such series can produce extremely good approximations to the values of…

Number Theory · Mathematics 2021-10-26 Gleb Beliakov , Yuri Matiyasevich

This paper improves algorithms given in math.CO/0012036. Although the graph (digraph) becomes non-random as the algorithm proceeds, the probability for success stays the same. We also give examples.

Combinatorics · Mathematics 2007-05-23 Howard Kleiman

As well known, the important hypothesis formulated by B.G. RIEMANN in 1859 states that all non-trivial zeroes of the Zeta function $Z(s)=\sum_{n=1}^{\infty } n^{-s}$ should fall on the Critical Line (C.L.) $Re(s)=\frac{1}{2}$.\\ Although…

General Mathematics · Mathematics 2019-02-19 Michele Fanelli , Alberto Fanelli

In this paper, we define the geometric median of a probability measure on a Riemannian manifold, give its characterization and a natural condition to ensure its uniqueness. In order to calculate the median in practical cases, we also…

Differential Geometry · Mathematics 2019-02-20 Le Yang

The validity of the Riemann Hypothesis (RH) on the location of the non-trivial zeros of the Riemann $\zeta$-function is directly related to the growth of the Mertens function $M(x) \,=\,\sum_{k=1}^x \mu(k)$, where $\mu(k)$ is the M\"{o}bius…

Number Theory · Mathematics 2021-11-23 Giuseppe Mussardo , Andre LeClair

There are many formulations of problems that have been proven to be equivalent to the Riemann Hypothesis in modern mathematics. In this paper we look at the formulation of an inequality derived by Robin in 1984 that proves the Riemann…

Number Theory · Mathematics 2020-02-20 William McCann

Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy…

Machine Learning · Statistics 2020-10-16 Jackson Gorham , Lester Mackey
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