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

F
Fortinet All Blogs
aimingoo的专栏
aimingoo的专栏
V
Visual Studio Blog
罗磊的独立博客
爱范儿
爱范儿
J
Java Code Geeks
博客园 - 司徒正美
N
Netflix TechBlog - Medium
Microsoft Security Blog
Microsoft Security Blog
美团技术团队
小众软件
小众软件
Google DeepMind News
Google DeepMind News
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
V
V2EX
博客园 - 聂微东
云风的 BLOG
云风的 BLOG
WordPress大学
WordPress大学
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Jina AI
Jina AI
Y
Y Combinator Blog
博客园 - 叶小钗
人人都是产品经理
人人都是产品经理
Martin Fowler
Martin Fowler
Vercel News
Vercel News

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 asymptotics of two non-uniform recursive tree models
Ella Hiesmayr · 2017-10-04 · via math.PR updates on arXiv.org

In this thesis the properties of two kinds of non-uniform random recursive trees are studied. In the first model weights are assigned to each node, thus altering the attachment probabilities. We will call these trees weighted recursive trees. In the second model a different distribution rather than the uniform one is chosen on the symmetric group, namely a riffle shuffle distribution. These trees will be called biased recursive trees. For both of these models the number of branches, the number of leaves, the depth of nodes and some other properties are studied. The focus is on asymptotic results and the comparison with uniform random recursive trees. It will be shown that the studied properties of weighted recursive trees are close to uniform recursive trees in many cases when the number of nodes increases. In contrast biased recursive trees show a different behaviour but approach uniform recursive trees depending on the parameters of the riffle shuffle distribution.