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

U
Unit 42
博客园 - 司徒正美
V
Visual Studio Blog
博客园 - 【当耐特】
T
Tailwind CSS Blog
美团技术团队
博客园 - 叶小钗
Jina AI
Jina AI
宝玉的分享
宝玉的分享
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
雷峰网
雷峰网
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
人人都是产品经理
人人都是产品经理
T
The Blog of Author Tim Ferriss
P
Proofpoint News Feed
Microsoft Security Blog
Microsoft Security Blog
Y
Y Combinator Blog
GbyAI
GbyAI
大猫的无限游戏
大猫的无限游戏
Martin Fowler
Martin Fowler
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
腾讯CDC

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
One dimensional random walk killed on a finite set
Kohei Uchiyama · 2016-03-07 · via math.PR updates on arXiv.org

We study the transition probability, say $p_A^n(x,y)$, of a one-dimensional random walk on the integer lattice killed when entering into a non-empty finite set $A$. The random walk is assumed to be irreducible and have zero mean and a finite variance $σ^2$. We derive the asymptotic form of $p_A^n(x, y)$ for large $n$ valid uniformly in the regime characterized by the conditions $|x|\vee |y| =O(\sqrt n)$ and $|x|\wedge |y|= o(\sqrt n)$, in which $p^A_t({\bf x},{\bf y})$ behaves for large $n$ like $[g_A^{+}(x)\hat g_{A}^{\,+}(y) + g_A^-(x)\hat g_{A}^{\,-}(y)] (σ^{2}/2n) p^n(y-x)$. Here $p^n(y-x)$ is the transition kernel of the random walk (without killing); $g^\pm_A$ are the Green functions for the "exterior" of $A$ with "pole at $\pm \infty$" normalized so that $g^\pm_A(x) \sim 2|x|/σ^2$ as $x \to \pm\infty$; and $\hat g_A^{\, \pm}$ are the corresponding Green functions for the time-reversed walk.