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 - 三生石上(FineUI控件)
D
DataBreaches.Net
博客园_首页
J
Java Code Geeks
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
罗磊的独立博客
腾讯CDC
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog
D
Docker
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
A
About on SuperTechFans
博客园 - 聂微东
Stack Overflow Blog
Stack Overflow Blog
WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
G
Google Developers Blog
博客园 - 司徒正美
aimingoo的专栏
aimingoo的专栏
小众软件
小众软件
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 叶小钗
M
MIT News - Artificial intelligence
Recent Announcements
Recent Announcements

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
Nonstationary moments for queuing systems
E. A. Golovastova · 2019-10-07 · via math.PR updates on arXiv.org

In this paper, we prove the exponential convergence of the non-stationary moment of a random variable that defines the virtual waiting time in the mass service system M\G\1\ $\infty $, where the distribution of the service time satisfies the Kramer condition and system load factor $ρ< 1$, to the value of the virtual waiting time in the stationary mode in this system. We consider the virtual waiting time as a regenerative process, which regenerative periods is the sum of busy period and period of time, when the system is free. We show, that the busy period in such system satisfies the Kramer condition, so, period of the virtual waiting time too. Using this, we prof the the exponential convergence of the non-stationary moment of the virtual waiting time distribution $ φ(t)$ to its value in the stationary mode. Also, we show, that the results from the general case is still true for the M\M\1\ $\infty $ system as a special case of the M\G\1\ $\infty$. Here we use explicit expression for the virtual waiting time distribution in such system and some Bessel functions properties to get the same convergence.