Article
Version 2
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Minimum Uncertainty as Bayesian Network Model Selection Principle
Version 1
: Received: 14 February 2022 / Approved: 21 February 2022 / Online: 21 February 2022 (14:13:26 CET)
Version 2 : Received: 7 June 2023 / Approved: 7 June 2023 / Online: 7 June 2023 (13:20:18 CEST)
Version 2 : Received: 7 June 2023 / Approved: 7 June 2023 / Online: 7 June 2023 (13:20:18 CEST)
How to cite: Gogoshin, G.; Rodin, A. Minimum Uncertainty as Bayesian Network Model Selection Principle. Preprints 2022, 2022020254. https://doi.org/10.20944/preprints202202.0254.v2 Gogoshin, G.; Rodin, A. Minimum Uncertainty as Bayesian Network Model Selection Principle. Preprints 2022, 2022020254. https://doi.org/10.20944/preprints202202.0254.v2
Abstract
In this paper study, we develop a Bayesian Network model selection principle that address addresses the incommensurability of network features obtained from incongruous datasets and overcomes performance irregularities of the Minimum Description Length model selection principle. This is achieved (i) by approaching model evaluation as a classification problem, (ii) by estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) by utilizing this error estimate to penalize uncertainty in the Minimum Uncertainty (MU) model selection principle. We validate our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on a tRNA structural biology example.
Keywords
Bayesian Networks; probabilistic networks; conditional independence; model selection criteria; mutual information; sampling error; statistical uncertainty; MDL; BIC; AIC; BD; tRNA
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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