arXiv (All)
open access
A Balanced Parametrization of Correlation Matrices
We introduce the balanced correlation parametrization (BCP), which maps positive-definite correlation matrices one-to-one onto the set of symmetric hollow matrices. A key feature of the parametrization is that both the transformation and its inverse are available in closed form. It combines marginal
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
- CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope#1297958
- A variational approach to the generalized Fisher transformation of correlation matrices#1013239
- Negativity-preserving transforms of tuples of symmetric matrices#139058
- Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold#1104686
- BLOC: A Global Optimization Framework for Sparse Covariance Estimation with Non-Convex Penalties#1062872
Record · ID 1325407
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