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 - Franky
U
Unit 42
MyScale Blog
MyScale Blog
B
Blog
阮一峰的网络日志
阮一峰的网络日志
量子位
IT之家
IT之家
The GitHub Blog
The GitHub Blog
F
Fortinet All Blogs
Recent Announcements
Recent Announcements
V
Visual Studio Blog
G
Google Developers Blog
Last Week in AI
Last Week in AI
雷峰网
雷峰网
博客园 - 聂微东
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
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
J
Java Code Geeks
博客园 - 司徒正美
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
月光博客
月光博客
aimingoo的专栏
aimingoo的专栏

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
Fluctuations of Schensted row insertion
Mikołaj Marciniak, Piotr Śniady · 2023-02-08 · via math.PR updates on arXiv.org

We investigate asymptotic probabilistic phenomena arising from the application of the Schensted row insertion algorithm, a key component of the Robinson-Schensted-Knuth (RSK) correspondence, to random inputs. Our analysis centers on a random tableau $T$ with a given shape $λ$, which may itself be random or deterministic. We examine the stochastic properties of the position of the new box created when inserting a deterministic entry into $T$. Specifically, we focus on the fluctuations of this position around its expected value as the size of the Young diagram $λ$ approaches infinity. Our findings reveal that these fluctuations are asymptotically Gaussian, with the mean and variance expressed in terms of Kerov's transition measure of the diagram $λ$. An important application of this analysis is the RSK algorithm applied to a finite, long sequence of independent, identically distributed random variables. While there remains a gap in the reasoning for this case, we present an explicit conjecture regarding its behavior.