arXiv (All)
open access
Measure and integration
This is an introduction to measure theory, integration and function spaces, with all the needed preliminaries included, and with some applications included as well. We first discuss some basic motivations, coming from discrete probability, that we develop in detail, as a preliminary to general measu
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
- Measure theory without infinities#926750
- Measure Theory and Abstract Integration Volume VI: Lebesgue Measure, Lp Spaces, Convergence Theorems, Abstract Probability, and Applications#990587
- Consequences of Assigning Non-Measurable Sets Imprecise Probabilities#90181
- Negative $β$-transformations: invariant measures, matching properties and associated subshifts#1129198
- Free probability via entropic optimal transport#1342276
Record · ID 1200763
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