arXiv (All)
open access
Matrix Graphical Model Via Joint Estimation of Partial Correlations
Matrix graphical models aim to characterize conditional dependence structures in matrix-variate data under a separable covariance assumption. In this framework, the precision matrix is decomposed as a Kronecker product, enabling separate modeling of undirected graphs across row and column domains. E
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
- Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships#985448
- Bayesian Inference for High-dimensional Time Series with a Stationary Directed Acyclic Graphical Structure#821692
- Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks#19023
- Rectangular Matrix Multiplication in the Low-Bandwidth Model#259430
- Riemannian Optimization for Hadamard Products of Low-Rank Matrices#798930
Record · ID 1014306
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