Document Detail


The development of posterior probability models in risk-based integrity modeling.
MedLine Citation:
PMID:  20163559     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
There is a need for accurate modeling of mechanisms causing material degradation of equipment in process installation, to ensure safety and reliability of the equipment. Degradation mechanisms are stochastic processes. They can be best described using risk-based approaches. Risk-based integrity assessment quantifies the level of risk to which the individual components are subjected and provides means to mitigate them in a safe and cost-effective manner. The uncertainty and variability in structural degradations can be best modeled by probability distributions. Prior probability models provide initial description of the degradation mechanisms. As more inspection data become available, these prior probability models can be revised to obtain posterior probability models, which represent the current system and can be used to predict future failures. In this article, a rejection sampling-based Metropolis-Hastings (M-H) algorithm is used to develop posterior distributions. The M-H algorithm is a Markov chain Monte Carlo algorithm used to generate a sequence of posterior samples without actually knowing the normalizing constant. Ignoring the transient samples in the generated Markov chain, the steady state samples are rejected or accepted based on an acceptance criterion. To validate the estimated parameters of posterior models, analytical Laplace approximation method is used to compute the integrals involved in the posterior function. Results of the M-H algorithm and Laplace approximations are compared with conjugate pair estimations of known prior and likelihood combinations. The M-H algorithm provides better results and hence it is used for posterior development of the selected priors for corrosion and cracking.
Authors:
Premkumar N Thodi; Faisal I Khan; Mahmoud R Haddara
Related Documents :
23109469 - How do you recruit and retain a prebirth cohort? lessons learnt from growing up in new ...
21503649 - Investigating human audio-visual object perception with a combination of hypothesis-gen...
12413239 - Do you know your total cholesterol (tc) number?
24999529 - Structured expert judgment to characterize uncertainty between pm2.5 exposure and morta...
23238019 - The reliability of a rugby league movement simulation protocol (rlmsp-i) designed to re...
2790119 - Probits of mixtures.
23654409 - Metrics for vector quantization-based parametric speech enhancement and separation.
25639 - A comparative study of five commercial reagents for the coulter model s: a proposed met...
23722409 - Nanoparticles do not penetrate human skin-a theoretical perspective.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Validation Studies     Date:  2010-02-15
Journal Detail:
Title:  Risk analysis : an official publication of the Society for Risk Analysis     Volume:  30     ISSN:  1539-6924     ISO Abbreviation:  Risk Anal.     Publication Date:  2010 Mar 
Date Detail:
Created Date:  2010-05-21     Completed Date:  2010-09-10     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8109978     Medline TA:  Risk Anal     Country:  United States    
Other Details:
Languages:  eng     Pagination:  400-20     Citation Subset:  IM    
Affiliation:
Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, Canada A1B3X5. premkumar.nt@gmail.com
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms
Base Sequence
Humans
Markov Chains
Models, Biological*
Physiological Processes
Probability
Risk
Risk Assessment
Uncertainty

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Experimental Evidence Against the Paradigm of Mortality Risk Aversion.
Next Document:  Beyond (financial) accessibility: inequalities within the medicalisation of infertility.