惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 【当耐特】
小众软件
小众软件
S
SegmentFault 最新的问题
GbyAI
GbyAI
量子位
爱范儿
爱范儿
L
LangChain Blog
Vercel News
Vercel News
A
About on SuperTechFans
腾讯CDC
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
月光博客
月光博客
博客园 - 聂微东
Stack Overflow Blog
Stack Overflow Blog
H
Help Net Security
U
Unit 42
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
V2EX
V
Visual Studio Blog
美团技术团队
D
DataBreaches.Net
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
The Expiring Coupon Collector: Sliding-Window Surjection ...
Christopher D. Long · 2026-04-29 · via math.PR updates on arXiv.org

We study the coupon collector with deterministic expiration: one coupon is drawn at each time, and each coupon remains active for exactly $M$ draws. Completion occurs when all $n$ coupon types are simultaneously active. Equivalently, the current length-$M$ sliding window of draws must contain all $n$ types. The central object is not the one-time probability that a random window is onto, but the stationary flux of new entries into the onto-window set. We compute this flux exactly: \[ μ_{n,M} =\Pbb(W_{t-1}\text{ is not onto},\ W_t\text{ is onto}) =\frac{(n-1)(n-1)!S(M-1,n-1)}{n^M}, \] where $S(\cdot,\cdot)$ denotes a Stirling number of the second kind. Under a quantitative subcritical separation condition, satisfied in particular by every fixed integer scale $M=\floor{αn\log n}$, $0<α<1$, we prove local declumping and obtain \[ μ_{n,M}T_{n,M}\Rightarrow \Exp(1). \] For the fixed subcritical scale $M=\floor{αn\log n}$, $0<α<1$, this gives the logarithmic scale \[ \log T_{n,M}=n^{1-α}+o_{\mathbb P}(n^{1-α}), \qquad \log \Ebb T_{n,M}=n^{1-α}+o(n^{1-α}), \] and, when $α>1/2$, the sharper normalization \[ n^{-α}e^{-n^{1-α}}T_{n,M}\Rightarrow \Exp(1), \qquad \Ebb T_{n,M}\sim n^αe^{n^{1-α}}. \] Thus the leading scale proposed in the Math StackExchange discussion is made rigorous; the exact finite-$n$ flux gives the canonical normalization throughout the subcritical range. The result is a sliding-window companion to rare-void entry-flux methods for nonmonotone coupon collectors.