Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model

2016 ◽  
Vol 150 ◽  
pp. 136-146 ◽  
Author(s):  
Márcio das Chagas Moura ◽  
Rafael Valença Azevedo ◽  
Enrique López Droguett ◽  
Leandro Rego Chaves ◽  
Isis Didier Lins ◽  
...  
Keyword(s):  
Population Variability ◽  
Expected Number ◽  
Variability Analysis

Corrigendum to “Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model” [Reliab. Eng. Syst. Saf. 150 (2016) 136–146]

2016 ◽  
Vol 154 ◽  
pp. 234 ◽  
Author(s):  
Márciodas Chagas Moura ◽  
Rafael Valença Azevedo ◽  
Enrique López Droguett ◽  
Leandro Chaves Rêgo ◽  
Isis Didier Lins ◽  
...  
Keyword(s):  
Population Variability ◽  
Expected Number ◽  
Variability Analysis

Treatment of Partial Homogeneity in Population Variability Analysis of Data

2004 ◽  
pp. 1044-1050
Author(s):  
Frank J. Groen ◽  
Ali Mosleh ◽  
Enrique L. Droguett
Keyword(s):  
Population Variability ◽  
Variability Analysis ◽  
Partial Homogeneity

scholarly journals Bayesian assessment of the variability of reliability measures

2006 ◽  
Vol 26 (1) ◽  
pp. 109-127 ◽  
Author(s):  
Enrique López Droguett ◽  
Frank J. Groen ◽  
Ali Mosleh
Keyword(s):  
Failure Rate ◽  
Failure Probability ◽  
Bayesian Updating ◽  
Estimation Procedure ◽  
Population Variability ◽  
Probability Assessment ◽  
Time Data ◽  
Reliability Measures ◽  
Variability Analysis ◽  
Variability Model

Population variability analysis, also known as the first stage in two-stage Bayesian updating, is an estimation procedure for the assessment of the variability of reliability measures among a group of sub-populations of similar systems. The estimated variability distributions are used as prior distributions in system-specific Bayesian updates. In this paper we present a Bayesian approach for population variability analysis involving the use of non-conjugate variability models that works over a continuous, rather than the discretized, variability model parameter space. The cases to be discussed are the ones typically encountered by the reliability practitioner: run-time data for failure rate assessment, demand-based data for failure probability assessment, and expert-based evidence for failure rate and failure probability analysis. We outline the estimation procedure itself as well as its link with conventional Bayesian updating procedures, describe the results generated by the procedures and their behavior under various data conditions, and provide numerical examples.

The combined use of data and expert estimates in population variability analysis

2004 ◽  
Vol 83 (3) ◽  
pp. 311-321 ◽  
Author(s):  
Enrique Lopez Droguett ◽  
Frank Groen ◽  
Ali Mosleh
Keyword(s):  
Population Variability ◽  
Variability Analysis ◽  
Use Of Data ◽  
Combined Use

Should I stay or should I go? Intra-population variability in movement behaviour of wide-ranging and resident coastal fishes

2019 ◽  
Vol 619 ◽  
pp. 111-124
Author(s):  
JQ Maggs ◽  
PD Cowley ◽  
SN Porter ◽  
AR Childs
Keyword(s):  
Population Variability ◽  
Movement Behaviour

Variability Analysis of Climate Extreme Index using Downscaled Multi-models and Grid-based CMIP5 Climate Change Scenario Data

2020 ◽  
Vol 11 (2) ◽  
pp. 123-132
Author(s):  
Jae-Pil Cho ◽  
Jae-Uk Kim ◽  
Soon-Kun Choi ◽  
Sye-Woon Hwang ◽  
Hui-Cheul Jung
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Extreme Index ◽  
Change Scenario ◽  
Climate Extreme ◽  
Variability Analysis ◽  
Grid Based
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