Zholdasbayeva, MoldirZarikas, Vasilios2022-04-272022-04-272021Zholdasbayeva, M., & Zarikas, V. (2021). A Comparison of Bayesian and Frequentist Approaches for the Case of Accident and Safety Analysis, as a Precept for All AI Expert Models. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence. 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010315810541065https://www.scitepress.org/Link.aspx?doi=10.5220/0010315810541065https://doi.org/10.5220/0010315810541065http://nur.nu.edu.kz/handle/123456789/6124Statistical modelling techniques are widely used in accident studies. It is a well-known fact that frequentist statistical approach includes hypothesis testing, correlations, and probabilistic inferences. Bayesian networks, which belong to the set of advanced AI techniques, perform advanced calculations related to diagnostics, prediction and causal inference. The aim of the current work is to present a comparison of Bayesian and Regression approaches for safety analysis. For this, both advantages and disadvantages of two modelling approaches were studied. The results indicated that the precision of Bayesian network was higher than that of the ordinal regression model. However, regression analysis can also provide understanding of the information hidden in data. The two approaches may suggest different significant explanatory factors/causes, and this always should be taken into consideration. The obtained outcomes from this analysis will contribute to the existing literature on safety science and accident analysis.enAttribution-NonCommercial-ShareAlike 3.0 United StatesArtificial Intelligence with UncertaintyBayesian NetworksSupervised LearningRegression MethodFrequentist StatisticsType of access: Open AccessArticle