5 papers
Decomposition for Bayesian Networks: Local and Parallel Inference
Pei Heng, Xinyi Hu, Yi Sun
Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a…
Revisiting Madigan and Mosurski: Collapsibility via Minimal Separators
Pei Heng, Shiyuan He, Yi Sun +1
Collapsibility provides a principled approach for dimension reduction in contingency tables and graphical models. Madigan and Mosurski (1990) pioneered the study of minimal collaps…
Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls
Yuxin Deng, Yi Sun, Zhiming Li +1
This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed…
An efficient recursive decomposition algorithm for undirected graphs
Pei Heng, Yi Sun, Jianhua Guo
The decomposition of undirected graphs simplifies complex problems by breaking them into solvable subgraphs, following the philosophy of divide and conquer. This paper investigates…
Structural Dimension Reduction in Bayesian Networks
Pei Heng, Yi Sun, Jianhua Guo
This work introduces a novel technique, named structural dimension reduction, to collapse a Bayesian network onto a minimum and localized one while ensuring that probabilistic infe…