arXiv (All)
open access
Robustness in the Poisson Boolean model with convex grains
We study the Poisson Boolean model where the grains are random convex bodies with a rotation-invariant distribution. A grain distribution is dense if the union of the grains covers the entire space and robust if the union of the grains has an unbounded connected component irrespective of the intensi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Universality for the extreme eigenvalues of Laplacian random matrices#1233115
- Higher-Order Gaussian Approximations on Path Space for the Simple Symmetric Exclusion Process#1297660
- Moment Ambiguity and the Limits of Robust Stochastic Optimization#1105648
- Algorithmic threshold for high-dimensional projection pursuit I: general theory#794206
- Algorithmic threshold for high-dimensional projection pursuit I: general theory#790187
Record · ID 1328258
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive