A comparison of Bayesian and frequentist approaches for the case of accident and safety analysis, as a precept for all AI expert models

Moldir Zholdasbayeva, Vasilios Zarikas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Statistical 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.

Original languageEnglish
Title of host publicationICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages1054-1065
Number of pages12
ISBN (Electronic)9789897584848
Publication statusPublished - 2021
Event13th International Conference on Agents and Artificial Intelligence, ICAART 2021 - Virtual, Online
Duration: Feb 4 2021Feb 6 2021

Publication series

NameICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference13th International Conference on Agents and Artificial Intelligence, ICAART 2021
CityVirtual, Online
Period2/4/212/6/21

Keywords

  • Artificial intelligence with uncertainty
  • Bayesian networks
  • Causal analysis
  • Elevator accidents
  • Frequentist statistics
  • Regression method
  • Safety rules
  • Supervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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