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 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
小众软件
小众软件
T
Tailwind CSS Blog
博客园 - 聂微东
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
人人都是产品经理
人人都是产品经理
V
Visual Studio Blog
罗磊的独立博客
有赞技术团队
有赞技术团队
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Jina AI
Jina AI
量子位
云风的 BLOG
云风的 BLOG
Recent Announcements
Recent Announcements
Hugging Face - Blog
Hugging Face - Blog
P
Proofpoint News Feed
N
Netflix TechBlog - Medium
GbyAI
GbyAI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
腾讯CDC
美团技术团队

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
Fractal-Dimensional Properties of Subordinators
Adam Barker · 2017-06-21 · via math.PR updates on arXiv.org

This work looks at the box-counting dimension of sets related to subordinators (non-decreasing Lévy processes). It was recently shown in [Savov, 2014] that almost surely $\lim_{δ\to0}U(δ)N(t,δ) = t$, where $N(t,δ)$ is the minimal number of boxes of size at most $δ$ needed to cover a subordinator's range up to time $t$, and $U(δ)$ is the subordinator's renewal function. Our main result is a central limit theorem (CLT) for $N(t,δ)$, complementing and refining work in [Savov, 2014]. Box-counting dimension is defined in terms of $N(t,δ)$, but for subordinators we prove that it can also be defined using a new process obtained by shortening the original subordinator's jumps of size greater than $δ$. This new process can be manipulated with remarkable ease in comparison to $N(t,δ)$, and allows better understanding of the box-counting dimension of a subordinator's range in terms of its Lévy measure, improving upon [Corollary 1, Savov, 2014]. Further, we shall prove corresponding CLT and almost sure convergence results for the new process.