Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships
Many scientific graphs attach several variables to each node, so a single scalar edge weight cannot describe direction-dependent interactions. We model each edge by a symmetric positive-definite (SPD) matrix and infer a posterior over matrix-valued graph geometries, which we call the Bayesian matrix-valued graph (BMVG). We ask how these interactions reconfigure across contexts: how large the change is and which multivariate directions strengthen or weaken. The geodesic distance induced by the affine-invariant Riemannian metric (AIRM) quantifies deformation magnitude and generalized eigenvalues resolve its signed directions.Against fused graphical lasso, Bayesian multiple-GGM, and common p...
| Authors | Papri Dey |
|---|---|
| Primary category | stat.ME |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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