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

Recent Announcements
Recent Announcements
V
Visual Studio Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
云风的 BLOG
云风的 BLOG
Microsoft Security Blog
Microsoft Security Blog
博客园 - 司徒正美
Y
Y Combinator Blog
Stack Overflow Blog
Stack Overflow Blog
雷峰网
雷峰网
小众软件
小众软件
GbyAI
GbyAI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
aimingoo的专栏
aimingoo的专栏
MyScale Blog
MyScale Blog
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
腾讯CDC
A
About on SuperTechFans
宝玉的分享
宝玉的分享
WordPress大学
WordPress大学
B
Blog RSS Feed
G
Google Developers Blog
量子位
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - 三生石上(FineUI控件)

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Buhmann covariance functions, their compact supports, and...
E. Porcu, P. Zastavyi, M. Bevilacqua · 2016-06-30 · via math.ST updates on arXiv.org

We consider the Buhmann class of compactly supported radial basis functions, whih includes a wealth of special cases that have been studied in both numerical analysis and spatial statistics literatures. In particular, the celebrated Wu, Wendland and Missing Wendland functions are notable special cases of this class. We propose a very simple difference operator and show the conditions for which the application of it to Buhmann functions preserves positive definiteness on $m$-dimensional Euclidean spaces. We also show that the application of the difference operator increases smoothness at the origin, whilst keeping positive definiteness in the same $m$-dimensional Euclidean space, as well as compact support. Thus, our operator is a competitor of the celebrated Mont{é}e operator, which allows to increase the smoothness at the origin, at the expense of losing positive definiteness in the space where the radial basis function is originally defined. The proofs of our results highlight surprising connections with past literatures on celebrated class of functions. Amongst them, absolute and completely monotone functions.