TL;DR
This paper critically reexamines the Bayesian K2 network scoring metric, revealing counterintuitive results and proposing a new family of noninformative priors to improve network scoring consistency.
Contribution
It introduces a new family of noninformative priors for the K2 metric, addressing issues with its application to simple networks.
Findings
Counterintuitive results from K2 metric application
Proposed noninformative priors for equal network scoring
Improved understanding of Bayesian network scoring
Abstract
This paper examines the "K2" network scoring metric of Cooper and Herskovits. It shows counterintuitive results from applying this metric to simple networks. One family of noninformative priors is suggested for assigning equal scores to equivalent networks.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Complex Network Analysis Techniques · Opinion Dynamics and Social Influence