Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor

Takeda, Satoshi; Kitada, Takanori

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2024, 10(4), 041203

Number of Access:245（2025-08-14 03:37 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97211

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Title
Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor
Creator
NRID
1000040786141 1000040786141
researchmap permalink
satoshi_takeda satoshi_takeda
Scopus Author ID
55263162800 55263162800
CreatorName
Takeda, Satoshi
NRID
1000060263208 1000060263208
researchmap permalink
read0185260 read0185260
Scopus Author ID
7102744350 7102744350
CreatorName
Kitada, Takanori
Description
Takeda S., Kitada T.. Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 10, 041203 (2024); https://doi.org/10.1115/1.4065531.
Abstract
The human error probabilities (HEP) can be estimated using multipliers that correspond to the level of performance shaping factors (PSFs) in the human reliability analysis (HRA). This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. Markov Chain Monte Carlo (MCMC) and Bayesian Monte Carlo (BMC) are used as Bayesian inference methods based on Monte Carlo techniques. MCMC is utilized to obtain the posterior distribution of the multipliers. BMC is used for the estimation of the moments of the posterior distribution such as the mean and variance. The results obtained by MCMC and that by BMC well agree with the reference results. As a case study, the data assimilation was performed using the results of the simulator experiment of Halden reactor. The results show that the multiplier changes by the result of a particular scenario and HEP of another scenario that uses the same multiplier also changes by data assimilation. Also, in the case study, the correlation between multipliers is obtained by the data assimilation and the correlation contributes to the reduction of uncertainty of HEP.
Publisher
American Society of Mechanical Engineers (ASME)
Source Title
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume (Issue)
10(4)
Page
041203
Date of Issued
2024-06-20
Language
English
Handle URL
https://hdl.handle.net/11094/97211
PISSN
23329017
eISSN
23329025
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1115/1.4065531
Access Rights
open access
oaire:version
Accepted Manuscript
Category
学術雑誌論文 Journal Article