Authors:
Moldir Zholdasbayeva
and
Vasilios Zarikas
Affiliation:
Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr ave. 53, Nur-Sultan 010000, Kazakhstan
Keyword(s):
Artificial Intelligence with Uncertainty, Bayesian Networks, Supervised Learning, Regression Method, Frequentist Statistics, Causal Analysis, Elevator Accidents, Safety Rules.
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.
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