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

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
云风的 BLOG
云风的 BLOG
Microsoft Security Blog
Microsoft Security Blog
WordPress大学
WordPress大学
GbyAI
GbyAI
C
Check Point Blog
M
MIT News - Artificial intelligence
T
The Blog of Author Tim Ferriss
Jina AI
Jina AI
博客园 - 【当耐特】
U
Unit 42
月光博客
月光博客
腾讯CDC
Y
Y Combinator Blog
小众软件
小众软件
博客园_首页
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
The GitHub Blog
The GitHub Blog
博客园 - 聂微东
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
T
Tailwind CSS Blog

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
How to Study Reflected Brownian Motion in a Quadrant via ...
Sandro Franceschi · 2026-06-04 · via math.PR updates on arXiv.org

We survey a line of works studying semimartingale reflected Brownian motion in a quadrant, covering both the non-degenerate and degenerate settings. Two main situations are emphasized: the recurrent case, where an invariant measure exists, and the transient case, where the central objects are Green's functions (potential measures). These measures typically arise from Kolmogorov forward equations. For transient or killed models one is also interested in the Martin boundary of the process and, consequently, in all positive harmonic functions, which satisfy Kolmogorov backward equations. Depending on the geometry and parameters of the model, these harmonic functions often admit probabilistic interpretations in terms of absorption, escape, or drift to infinity. All these measures and functions are studied through kernel functional equations satisfied by their Laplace transforms. Several ways of solving these equations are reviewed, each leading to different types of results. Following the analytic approach developed for quarter-plane random walks by Fayolle, Iasnogorodski and Malyshev, a key preliminary step is the analytic continuation of the relevant Laplace transforms onto the complex algebraic curve defined by the zero set of the kernel. Carleman boundary value problem techniques then yield explicit contour-integral representations for the Laplace transforms. In special parameter regimes, Tutte's invariant method provides integral-free formulas and a sharp classification of the transforms according to their algebraic-differential complexity. Singularity analysis combined with saddle-point methods carried out on the kernel algebraic curve, produces precise two-dimensional asymptotics. Finally, in the degenerate setting, the compensation approach provides an alternative constructive method, allowing one to build the densities as an infinite series through explicit iterative corrections.