A COMPARISON OF BAYESIAN AND FREQUENTIST APPROACHES FOR THE CASE OF ACCIDENT AND SAFETY ANALYSIS, AS A PRECEPT FOR ALL AI EXPERT MODELS
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Date
2021
Authors
Zholdasbayeva, Moldir
Zarikas, Vasilios
Journal Title
Journal ISSN
Volume Title
Publisher
SCITEPRESS
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.
Description
Keywords
Artificial Intelligence with Uncertainty, Bayesian Networks, Supervised Learning, Regression Method, Frequentist Statistics, Type of access: Open Access
Citation
Zholdasbayeva, 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/10.5220/0010315810541065