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

月光博客
月光博客
Martin Fowler
Martin Fowler
博客园_首页
量子位
T
Tailwind CSS Blog
博客园 - Franky
G
Google Developers Blog
D
DataBreaches.Net
Vercel News
Vercel News
B
Blog
Recent Announcements
Recent Announcements
S
SegmentFault 最新的问题
M
MIT News - Artificial intelligence
爱范儿
爱范儿
博客园 - 【当耐特】
The Cloudflare Blog
H
Help Net Security
云风的 BLOG
云风的 BLOG
P
Proofpoint News Feed
C
Check Point Blog
有赞技术团队
有赞技术团队
Microsoft Security Blog
Microsoft Security Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

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
Spatial Point Pattern Analysis of the Unidentified Aerial...
Thibault Laurent</name> <arxiv:affiliation>GREMAQ</arxiv:a · 2015-09-02 · via math.ST updates on arXiv.org

We model the unidentified aerial phenomena observed in France during the last 60 years as a spatial point pattern. We use some public information such as population density, rate of moisture or presence of airports to model the intensity of the unidentified aerial phenomena. Spatial exploratory data analysis is a first approach to appreciate the link between the intensity of the unidentified aerial phenomena and the covariates. We then fit an inhomogeneous spatial Poisson process model with covariates. We find that the significant variables are the population density, the presence of the factories with a nuclear risk and contaminated land, and the rate of moisture. The analysis of the residuals shows that some parts of France (the Belgian border, the tip of Britany, some parts in the SouthEast , the Picardie and Haute-Normandie regions, the Loiret and Corr eze departments) present a high value of local intensity which are not explained by our model.