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

Engineering at Meta
Engineering at Meta
人人都是产品经理
人人都是产品经理
aimingoo的专栏
aimingoo的专栏
M
MIT News - Artificial intelligence
Recent Announcements
Recent Announcements
V
Visual Studio Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
MyScale Blog
MyScale Blog
Hugging Face - Blog
Hugging Face - Blog
宝玉的分享
宝玉的分享
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 叶小钗
博客园 - 聂微东
U
Unit 42
F
Fortinet All Blogs
Microsoft Security Blog
Microsoft Security Blog
GbyAI
GbyAI
IT之家
IT之家
The GitHub Blog
The GitHub Blog
Stack Overflow Blog
Stack Overflow Blog
MongoDB | Blog
MongoDB | Blog
Y
Y Combinator Blog
A
About on SuperTechFans
博客园 - 三生石上(FineUI控件)

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
On some integrable models in inhomogeneous space
Theodoros Assiotis · 2023-10-27 · via math.PR updates on arXiv.org

The purpose of this work is to build a framework that allows for an in-depth study of various generalisations to inhomogeneous space of models of Borodin-Ferrari, Dieker-Warren, Nordenstam, Warren-Windridge of interacting particles in interlacing arrays, both in discrete and continuous time, involving both Bernoulli and geometric jumps. The models can in addition be either time-inhomogeneous or particle-inhomogeneous. We show that the correlation functions of these models are determinantal and using this we prove a short-time asymptotic for these dynamics to the discrete Bessel point process. We moreover prove a number of closely related results. We prove that the autonomous, inhomogeneous in space and time, TASEP-like and pushTASEP-like particle systems on the edges of the array have explicit transition kernels and that from any deterministic initial condition their distributions are marginals of a determinantal measure. We prove a novel duality relation between dynamics in inhomogeneous space and dynamics with inhomogeneities on the level of the array. We extend the work of Nordenstam on the shuffling algorithm for domino tilings of the Aztec diamond and its relation to push-block dynamics in interlacing arrays to general weights on the tilings and then connect this, for a special class of weights, back to our previous results. We also consider non-intersecting walks in inhomogeneous space and time with fixed starting and end points and obtain a formula for their correlation functions, involving among other ingredients, an explicit Riemann-Hilbert problem. We then prove a limit theorem for the bottom lines in this line-ensemble, under some technical conditions. The main computational tool throughout this work is a natural generalisation of a Toeplitz matrix, that we call inhomogeneous Toeplitz-like matrix $\mathsf{T}_{\mathbf{f}}$ with (a possibly matrix-valued) symbol $\mathbf{f}$.