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A copula-based transitional markov chain monte carlo method for bayesian model updating

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  • Ma, Pengfei
  • Zhang, Yi
  • Cai, Enjian
  • Luo, Min
  • Guo, Jing
  • Guo, Tong

Abstract

This paper introduces the Copula-based Transitional Markov Chain Monte Carlo (CTMCMC) method, an innovative approach for sampling the posterior distribution in Bayesian model updating. Based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm, CTMCMC incorporates Copula functions into the proposal distribution, deviating from the conventional multivariate Gaussian sampler. This modification allows for more flexible and effective sampling from complex, high-dimensional probability distributions. The effectiveness of CTMCMC is demonstrated through three case studies, where the efficiency and accuracy of CTMCMC are highlighted, particularly in scenarios involving multimodal and high-dimensional distributions. However, the main challenge is the computational demand of high-dimensional copula functions. This study mainly validates the effectiveness of CTMCMC in Bayesian updating. Further research is encouraged to improve its computational efficiency.

Suggested Citation

  • Ma, Pengfei & Zhang, Yi & Cai, Enjian & Luo, Min & Guo, Jing & Guo, Tong, 2026. "A copula-based transitional markov chain monte carlo method for bayesian model updating," Reliability Engineering and System Safety, Elsevier, vol. 265(PB).
    • Handle: RePEc:eee:reensy:v:265:y:2026:i:pb:s0951832025007720
    DOI: 10.1016/j.ress.2025.111572
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