Model calibration: A hierarchical Bayesian approach

Bozorgzadeh, Nezam; Liu, Zhongqiang; Nadim, Farrokh; Lacasse, Suzanne

doi:10.1016/j.probengmech.2022.103379

Cited by 6 publications

(2 citation statements)

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“…Further details regarding the applications of HBA can be derived from references [63][64][65]. The HBA generates the most suitable values for the parameters of interest (e.g., θ t ) involving minimum uncertainty [63,[66][67][68]. As shown in Figure 5, the hierarchical Bayesian model uses groups of exchangeable information to deduce θ t , which is similar but does not need to be identical.…”

Section: Estimation Of the Probability Of Occurrence Using Hbamentioning

confidence: 99%

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Probabilistic Analysis of Basic Causes of Vessel–Platform Allision Accidents

Bhardwaj,

Teixeira,

Guedes Soares

2024

JMSE

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This paper proposes a methodology to estimate the probability of basic causes of allision accidents between vessels and offshore platforms that overcomes the problem of data scarcity required for causal analysis. The approach uses information derived from incidental data and expert elicitation, processed by a multiple attribute utility method and hierarchical Bayesian analysis. First, the methodology is detailed, briefly describing the adopted approaches. A dataset of allision incidents provided mainly by the UK Health and Safety Executive and other agencies is prepared. The features of the incidents’ causation in terms of the causal factors and basic causes are presented and discussed. A novel scheme is proposed to evaluate the annual occurrence rates of basic causes of accidents from the relative importance of each basic cause derived by the Deck of Cards method. Then, a hierarchical Bayesian analysis is conducted to predict the posterior distribution of the occurrence rate of each basic cause in the time frame under analysis. The proposed holistic methodology provides transparent estimates of allision causation probabilities from limited and heterogeneous datasets.

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