Results 21 to 30 of about 29,410 (152)
Debiasing piecewise deterministic Markov process samplers using couplings
Abstract Monte Carlo methods—such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers—provide asymptotically exact estimators of expectations under a target distribution. There is growing interest in alternatives to this asymptotic regime, in particular in constructing estimators that are exact in the limit of ...
Adrien Corenflos +2 more
wiley +1 more source
Compactifications of strata of differentials
Abstract In this informal expository note, we quickly introduce and survey compactifications of strata of holomorphic 1‐forms on Riemann surfaces, that is, spaces of translation surfaces. In the last decade, several of these have been constructed, studied, and successfully applied to problems.
Benjamin Dozier
wiley +1 more source
Abstract We consider a planar Coulomb gas ensemble of size N$N$ with the inverse temperature β=2$\beta =2$ and external potential Q(z)=|z|2−2clog|z−a|$Q(z)=|z|^2-2c \log |z-a|$, where c>0$c>0$ and a∈C$a \in \mathbb {C}$. Equivalently, this model can be realised as N$N$ eigenvalues of the complex Ginibre matrix of size (c+1)N×(c+1)N$(c+1) N \times (c+1)
Sung‐Soo Byun +2 more
wiley +1 more source
Explicit height estimates for CM curves of genus 2
Abstract In this paper, we make explicit the constants of Habegger and Pazuki's work from 2017 on bounding the discriminant of cyclic Galois CM fields corresponding to genus 2 curves with CM and potentially good reduction outside a predefined set of primes. We also simplify some of the arguments.
Linda Frey +2 more
wiley +1 more source
ABSTRACT In recent years, the study of sequential fractional differential equations (SFDEs) has become increasingly important in multiple domains of science and engineering. This work investigates a new class of boundary value problems (BVPs) characterized by nonlocal closed boundary conditions involving SFDEs with Caputo fractional integral operators.
Saud Fahad Aldosary +2 more
wiley +1 more source
Optimizing calibration designs with uncertainty in abilities
Abstract Before items can be implemented in a test, the item characteristics need to be calibrated through pretesting. To achieve high‐quality tests, it's crucial to maximize the precision of estimates obtained during item calibration. Higher precision can be attained if calibration items are allocated to examinees based on their individual abilities ...
Jonas Bjermo +2 more
wiley +1 more source
Fractional Gaussian Noise: Spectral Density and Estimation Methods
The fractional Brownian motion (fBm) process, governed by a fractional parameter H∈(0,1)$$ H\in \left(0,1\right) $$, is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn.
Shuping Shi, Jun Yu, Chen Zhang
wiley +1 more source
Simple Barban–Davenport–Halberstam type asymptotics for general sequences
Abstract We prove two estimates for the Barban–Davenport–Halberstam type variance of a general complex sequence in arithmetic progressions. The proofs are elementary, and our estimates are capable of yielding an asymptotic for the variance when the sequence is sufficiently nice, and is either somewhat sparse or is sufficiently like the integers in its ...
Adam J. Harper
wiley +1 more source
Moments of the Riemann zeta function at its local extrema
Abstract Conrey, Ghosh and Gonek studied the first moment of the derivative of the Riemann zeta function evaluated at the non‐trivial zeros of the zeta function, resolving a problem known as Shanks' conjecture. Conrey and Ghosh studied the second moment of the Riemann zeta function evaluated at its local extrema along the critical line to leading order.
Andrew Pearce‐Crump
wiley +1 more source
Fractional moments of L$L$‐functions and sums of two squares in short intervals
Abstract Let b(n)=1$b(n)=1$ if n$n$ is the sum of two perfect squares, and b(n)=0$b(n)=0$ otherwise. We study the variance of B(x)=∑n⩽xb(n)$B(x)=\sum _{n\leqslant x}b(n)$ in short intervals by relating the variance with the second moment of the generating function f(s)=∑n=1∞b(n)n−s$f(s)=\sum _{n=1}^{\infty } b(n)n^{-s}$ along Re(s)=1/2$\mathrm{Re}(s)=1/
Siegfred Baluyot, Steven M. Gonek
wiley +1 more source

