Nonparametric Bayesian networks are typically faithful in the total variation metric

We show that for a given DAG \(G\), among all observational distributions of Bayesian networks over \(G\) with arbitrary outcome spaces, the faithful distributions are `typical': they constitute a dense, open set with respect to the total variation metric. As a consequence, the set of faithful...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Boeken, Philip, ré, Patrick, Mooij, Joris M
Format: Article
Language:English
Subjects:
Bayesian analysis
Networks
Topology
Online Access:Get full text
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Summary:We show that for a given DAG \(G\), among all observational distributions of Bayesian networks over \(G\) with arbitrary outcome spaces, the faithful distributions are `typical': they constitute a dense, open set with respect to the total variation metric. As a consequence, the set of faithful distributions is non-empty, and the unfaithful distributions are nowhere dense. We extend this result to the space of Bayesian networks, where the properties hold for Bayesian networks instead of distributions of Bayesian networks. As special cases, we show that these results also hold for the faithful parameters of the subclasses of linear Gaussian -- and discrete Bayesian networks, giving a topological analogue of the measure-zero results of Spirtes et al. (1993) and Meek (1995). Finally, we extend our topological results and the measure-zero results of Spirtes et al. and Meek to Bayesian networks with latent variables.
ISSN:2331-8422