Random field modeling with insufficient data sets for probability analysis

Zhimin Xi, Byung C. Jung, Byeng D. Youn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


It has been widely acknowledged that consideration of the random field is quite significant to accurately predict variability in system performances. However, current approaches for characterizing the random field can only be applied to the situation with sufficient random field data sets and are not suitable to most engineering problems where the data sets are insufficient. The contribution of this paper is to mo del the random field based on the insufficient data sets such that sufficient data sets can be simulated or generated according to the random field modeling. Therefore, available random field characterization approaches and probability analysis methods can be used for probability analysis and design of many engineering problems with the lack of random field data sets. The proposed random field modeling is composed of two technical components including: 1) a Bayesian updating approach using the Markov Chain Monte Carlo (MCMC) method for modeling the random field based on available random field data sets; and 2) a Bayesian Copula dependence modeling approach for modeling statistical dependence of random field realizations at different measurement locations. A refrigerator assembly example is used to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2012 Annual Reliability and Maintainability Symposium, RAMS 2012 - Proceedings and Tutorials
StatePublished - 2012
Externally publishedYes
Event2012 Annual Reliability and Maintainability Symposium, RAMS 2012 - Reno, NV, United States
Duration: Jan 23 2012Jan 26 2012

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
ISSN (Print)0149-144X


Other2012 Annual Reliability and Maintainability Symposium, RAMS 2012
Country/TerritoryUnited States
CityReno, NV

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications


  • Bayesian Copula
  • Bayesian updating
  • insufficient data sets
  • probability analysis
  • random field


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