On weak convergence of Gaussian conditional distributions

Sarah Lumpp and Mathias Drton

Statistics & Probability Letters, 2025, vol. 226, issue C

Abstract: Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gaussian distributions converge to a singular Gaussian limit. Algebraically, this is due to the fact that at singular covariance matrices, Schur complements are not continuous functions of the matrix entries. Our results lay out special conditions under which convergence of Gaussian conditional distributions nevertheless occurs, and we exemplify how this allows one to reason about conditional independence in a new class of graphical models.

Keywords: Conditional distribution; Schur complement; Singular normal distribution; Matrix determinant lemma; Lyapunov equation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.spl.2025.110497

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