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

罗磊的独立博客
I
InfoQ
雷峰网
雷峰网
Hugging Face - Blog
Hugging Face - Blog
IT之家
IT之家
云风的 BLOG
云风的 BLOG
有赞技术团队
有赞技术团队
Martin Fowler
Martin Fowler
MyScale Blog
MyScale Blog
The GitHub Blog
The GitHub Blog
博客园_首页
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
G
Google Developers Blog
WordPress大学
WordPress大学
B
Blog
人人都是产品经理
人人都是产品经理
小众软件
小众软件
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
量子位
Apple Machine Learning Research
Apple Machine Learning Research
N
Netflix TechBlog - Medium
Last Week in AI
Last Week in AI
博客园 - 聂微东
Jina AI
Jina AI

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
Long paths and connectivity in {$1$}-independent random g...
A. Nicholas Day, Victor Falgas-Ravry, Robert Hancock · 2019-09-30 · via math.PR updates on arXiv.org

Given a graph $G$, a probability measure $μ$ on the subsets of the edge set of $G$ is said to be $1$-independent if events determined by edge sets that are at graph distance at least $1$ apart in $G$ are independent. Call such a probability measure a $1$-ipm on $G$, and denote by $\mathbf{G}_μ$ the associated random spanning subgraph of $G$. Let $\mathcal{M}_{1,\geqslant p}(G)$ (resp. $\mathcal{M}_{1,\leqslant p}(G)$) denote the collection of $1$-ipms $μ$ on $G$ for which each edge is included in $\mathbf{G}_μ$ with probability at least $p$ (resp. at most $p$). Let $\mathbb{Z}^2$ denote the square integer lattice. Balister and Bollobás raised the question of determining the critical value $p_{\star}=p_{1,c}(\mathbb{Z}^2)$ such that for all $p>p_{\star}$ and all $μ\in \mathcal{M}_{1,\geqslant p}(\mathbb{Z}^2)$, $\left(\mathbf{\mathbb{Z}^2}\right)_μ$ almost surely contains an infinite component. This can be thought of as asking for a $1$-independent analogue of the celebrated Harris--Kesten theorem. In this paper we investigate both this problem and connectivity problems for $1$-ipms more generally. We give two lower bounds on $p_{\star}$ that significantly improve on the previous bounds. Furthermore, motivated by the Russo--Seymour--Welsh lemmas, we define a $1$-independent critical probability for long paths and determine its value for the line and ladder lattices. Finally, for finite graphs $G$ we study $f_{1,G}(p)$ (respectively $F_{1,G}(p)$), the infimum (resp. supremum) over all $μ\in \mathcal{M}_{1,\geqslant p}(G)$ (resp. all $μ\in \mathcal{M}_{1,\leqslant p}(G)$) of the probability that $\mathbf{G}_μ$ is connected. We determine $f_{1,G}(p)$ and $F_{1,G}(p)$ exactly when $G$ is a path, a complete graph and a cycle of length at most $5$. Many new problems arise from our work, which are discussed in the final section of the paper.