Work

Revisiting Madigan and Mosurski: collapsibility via minimal separators

Year: 2026

Type: article

Abstract: Collapsibility provides a principled approach to dimension reduction in contingency tables and graphical models. Madigan & Mosurski (1990) pioneered the study of minimal collapsible sets in decomposable models, but existing algorithms for general graphs remain computationally demanding. We show that a model is collapsible on to a target set precisely when that set contains at least one minimal separator between its nonadjacent vertices. This insight motivates the close minimal separator absorption algorithm, which constructs minimal collapsible sets using only local separator searches at very low costs. Simulations confirm substantial efficiency gains, making collapsibility analysis practical in high-dimensional settings.

Source: Biometrika

DOI: 10.1093/biomet/asag024

Authors: Pei Heng, Shiyuan He, Yi Sun, Jianhua Guo

Institutions: Northeast Normal University, Beijing Technology and Business University, Xinjiang University of Finance and Economics

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China