A COMPARISON OF BAYESIAN AND FREQUENTIST APPROACHES FOR THE CASE OF ACCIDENT AND SAFETY ANALYSIS, AS A PRECEPT FOR ALL AI EXPERT MODELS

dc.contributor.authorZholdasbayeva, Moldir
dc.contributor.authorZarikas, Vasilios
dc.date.accessioned2022-04-27T10:27:14Z
dc.date.available2022-04-27T10:27:14Z
dc.date.issued2021
dc.description.abstractStatistical 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.en_US
dc.identifier.citationZholdasbayeva, 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/10.5220/0010315810541065en_US
dc.identifier.urihttps://www.scitepress.org/Link.aspx?doi=10.5220/0010315810541065
dc.identifier.urihttps://doi.org/10.5220/0010315810541065
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6124
dc.language.isoenen_US
dc.publisherSCITEPRESSen_US
dc.relation.ispartofseriesICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE;VOL 2
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectArtificial Intelligence with Uncertaintyen_US
dc.subjectBayesian Networksen_US
dc.subjectSupervised Learningen_US
dc.subjectRegression Methoden_US
dc.subjectFrequentist Statisticsen_US
dc.subjectType of access: Open Accessen_US
dc.titleA COMPARISON OF BAYESIAN AND FREQUENTIST APPROACHES FOR THE CASE OF ACCIDENT AND SAFETY ANALYSIS, AS A PRECEPT FOR ALL AI EXPERT MODELSen_US
dc.typeArticleen_US
workflow.import.sourcescience

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