Work

Comparative evaluation of score criteria for dynamic Bayesian Network structure learning

Year: 2025

Type: article

Abstract: Dynamic Bayesian Networks (DBNs) are probabilistic models with a directional structure employed to model temporal processes. Three approaches to DBN structure learning are constraint-based, score-based, and hybrid. The score criterion determined in the score-based and hybrid approach has a certain effect on structure learning and this study aims to examine their performance by diversifying the score criteria used in DBN structure learning in addition to the scores commonly used in the literature. Thus, the Akaike-based information criteria as Akaike Information Criterion (AIC), Consistent AIC (CAIC), Kullback-Leibler Information Criterion (KIC), AIC4, and the Bayesian-based information criteria as Bayesian Information Criterion (BIC), Adjusted BIC (BICadj), Haughton BIC (HBIC), BICQ were adapted into the DBN structure learning. The obtained results were discussed.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0336250

Authors: Aslı Yaman, Mehmet Ali Cengiz

Institutions: Istanbul Arel University, Imam Mohammad ibn Saud Islamic University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funder: Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University