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Research PaperResearchia:202607.02026

Characterizing and Identifying Separable Graphical Models

Christopher Meek

Abstract

We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of encoding independence structures arising from feedback, latent and selection mechanisms. In particular, we introduce separable graphs, in which each missing edge implies the existence of a separating set for its endpoints, and essentially separable graphs, those graphs separation equivalent to a separable graph. We sh...

Submitted: July 2, 2026Subjects: Statistics; Data Science

Description / Details

We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of encoding independence structures arising from feedback, latent and selection mechanisms. In particular, we introduce separable graphs, in which each missing edge implies the existence of a separating set for its endpoints, and essentially separable graphs, those graphs separation equivalent to a separable graph. We show that these models include many existing graph families used to define graphical models an provide several characterizations of separable graphs and essentially separable graphs. We also provide multiple characterizations of separation equivalence for separable graphs. One is a graphical characterization in terms of ordinary graph properties, extending earlier results for specific subfamilies Another is a separational characterization depending only on graph separation properties. Finally, we provide a canonical representation for the equivalence classes of essentially separable graphs and develop an algorithm that, under suitable assumptions, identifies the equivalence class of any essentially separable graph.


Source: arXiv:2607.01057v1 - http://arxiv.org/abs/2607.01057v1 PDF: https://arxiv.org/pdf/2607.01057v1 Original Link: http://arxiv.org/abs/2607.01057v1

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Date:
Jul 2, 2026
Topic:
Data Science
Area:
Statistics
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