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

Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
A
About on SuperTechFans
T
The Blog of Author Tim Ferriss
I
InfoQ
博客园_首页
G
Google Developers Blog
爱范儿
爱范儿
Last Week in AI
Last Week in AI
量子位
阮一峰的网络日志
阮一峰的网络日志
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
GbyAI
GbyAI
月光博客
月光博客
The GitHub Blog
The GitHub Blog
V
Visual Studio Blog
N
Netflix TechBlog - Medium
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 聂微东

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
From Directed Polymers in Spatial-correlated Environment ...
[Submitted on 24 Jul 2017 (v1), last revised 30 Aug 2026 (this v · 2017-07-24 · via math.PR updates on arXiv.org

View PDF HTML (experimental)

Abstract:We consider the limit behavior of partition function of directed polymers in random environment represented by linear model instead of a family of this http URL in $1+1$ dimensions. Under the assumption that the correlation decays algebraically, using the method developed in [Ann. Probab., 42(3):1212-1256, 2014], under a new scaling we show the scaled partition function as a process defined on $[0,1]\times\RR$, converges weakly to the solution to some stochastic heat equations driven by fractional Brownian field. The Hurst parameter is determined by the correlation exponent of the random environment. Here multiple Itô integral with respect to fractional Gaussian field and spectral representation of stationary process are heavily involved.

Submission history

From: Guanglin Rang [view email]
[v1] Mon, 24 Jul 2017 08:25:14 UTC (28 KB)
[v2] Wed, 18 Dec 2019 10:10:04 UTC (33 KB)
[v3] Sun, 30 Aug 2026 01:50:33 UTC (33 KB)