Block-Cholesky Spaces in CGGMs
- Block-Cholesky spaces (BC-spaces) are defined as linear subspaces of real symmetric matrices partitioned into blocks, enabling tractable Bayesian normalization in colored Gaussian graphical models.
- They leverage combinatorial conditions such as color-perfect elimination ordering and 2-path regularity to guarantee explicit closed-form Diaconis-Ylvisaker integrals for model selection.
- Their structure generalizes decomposable graphs and Bose–Mesner algebras, providing efficient computation and expanded applicability in symmetry-restricted statistical models.
Block-Cholesky spaces (BC-spaces) are a class of linear subspaces of real symmetric matrices structured by block decompositions, introduced to characterize families of colored Gaussian graphical models (CGGMs) that admit explicit closed-form normalizing constants for Bayesian structure learning. These spaces generalize the settings of decomposable graphs and one-color RCOP (random covariance under permutation) models, extending tractability to broader symmetry and sparsity constraints induced by colored graphs. The notion of BC-spaces is closely tied to combinatorial and algebraic conditions on colored graphs, and their analysis yields efficient computation of Diaconis-Ylvisaker integrals central to Gaussian graphical model selection (Chojecki et al., 23 Jan 2026).
1. Formal Structure and Definition
Let be a positive integer, and partition into ordered blocks of sizes . Any is written in block-matrix form with and . A linear subspace is a BC-space of rank if:
- (Z0) The identity , and decomposes as
where consists of block-diagonal matrices supported only on and consists of matrices supported on the off-diagonal "layer" blocks and their transposes.
- (Z1) For every , the block-lower-triangular truncation
satisfies .
A BC-space is said to be a diagonally-commutative BC-space (DCBC, also denoted cBC-space) if for all ,
where extracts the block-diagonal matrix. This implies that the subalgebras commute under the standard matrix product.
2. Algebraic and Combinatorial Characterization
BC-spaces arise as symmetry-restricted subspaces in CGGMs where precision matrices are both sparse and color-symmetric. Explicitly, for a colored graph on vertices, the RCON-space
is a BC-space if admits a color-perfect elimination ordering (cpeo) and satisfies the 2-path regularity condition.
Each is a Euclidean Jordan algebra under the product , with identity . The direct-sum and algebraic structure are enforced by the combinatorial properties of the colored graph, in particular by the block associated to the cpeo and regularity of edge colors.
DCBC-spaces further require symmetric 2-path count regularity, ensuring all commute and the algebraic structure is commutative, connecting them to commutative association schemes in the one-color case.
3. Combinatorial Conditions: Color-Perfect Elimination Ordering and 2-Path Regularity
Given a partition of the vertex set into color classes , a color-perfect elimination ordering (cpeo) is a permutation of such that for each and every , remains simplicial (its neighbors form a clique) in the induced subgraph on .
2-path regularity is defined via counts of "color paths": for an extended edge (possibly a loop) of color ,
with
It is required that is constant across all extended edges of the same color (M1). For the symmetric DCBC case, diagonal color symmetry for sharing a vertex-color (M2) is also imposed.
This framework generalizes decomposable graphs; for example, if a group acts generously transitively on each color class and on the edge colors, a peo of induces a cpeo satisfying all requirements.
4. Closed-Form Evaluation of Diaconis-Ylvisaker Normalizing Constants
For a BC-space of rank with subalgebras (Jordan rank ), the generalized Cholesky decomposition writes any as , . Decomposing via Peirce idempotents (with , each of matrix-rank ) and off-diagonal Peirce spaces (multiplicity ), the determinant and trace functionals factor:
- reduces to a quadratic form in the Cholesky parameters involving block matrices
The normalization integral
splits as a product of independent gamma-Gaussian integrals, admitting the closed-form
with collecting constants and the determinants computable via efficient "pivoted Gram–Cholesky" procedures. For DCBC-spaces, the required eigenstructure is found by diagonalizing a generic linear combination in , further reducing computational cost.
5. Example: One-Color Block-Cholesky Space
Consider , , the space of dimension 2. Taking $e = \frac{1}{\sqrt{2}}\begin{bmatrix}1&0\0&1\end{bmatrix}$, $f = \frac{1}{\sqrt{2}}\begin{bmatrix}0&1\1&0\end{bmatrix}$, an element is . The determinant is , so Cholesky variables , yield the explicit normalizing constant for :
where are the eigenvalues of .
6. Relationship to DCBC-Spaces and Bose–Mesner Algebras
DCBC-spaces are those BC-spaces for which the commutativity condition (Z2) holds. In the case of a single color on the complete graph, coincides with the Bose–Mesner algebra of an association scheme. Here, the idempotents correspond to minimal idempotents of the scheme, and the block decomposition aligns with the adjacency algebra. The explicit normalization formula reduces to the classical Wishart on a commutative algebra, whose structure constants are the association scheme’s intersection numbers and eigenvalues correspond to multiplicities.
Thus, BC-spaces strictly generalize both decomposable graphical model spaces and the commutative Bose–Mesner (one-color) case, recovering the classical results as special instances and extending explicit Bayesian normalization formulas to a broader class of colored graphical models (Chojecki et al., 23 Jan 2026).