Statistics > Machine Learning
[Submitted on 8 Jun 2022 (v1), last revised 4 Aug 2022 (this version, v2)]
Title:Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets
View PDFAbstract:We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are related but not homogeneous because they have been collected in different ways or from different populations.
In our previous work (Azzimonti, Corani and Scutari, 2021), we proposed a closed-form Bayesian Hierarchical Dirichlet score for discrete data that pools information across related data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. In this paper, we provide an analogous solution for learning a Bayesian network from continuous data using mixed-effects models to pool information across the related data sets. We study its structural, parametric, predictive and classification accuracy and we show that it outperforms both conditional Gaussian Bayesian networks (that do not perform any pooling) and classical Gaussian Bayesian networks (that disregard the heterogeneous nature of the data). The improvement is marked for low sample sizes and for unbalanced data sets.
Submission history
From: Marco Scutari [view email][v1] Wed, 8 Jun 2022 08:32:32 UTC (641 KB)
[v2] Thu, 4 Aug 2022 09:41:40 UTC (641 KB)
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