Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging

• Published in: Journal of engineering mechanics Vol. 133; no. 7; pp. 816 - 832
• Main Authors: Ching, Jianye, Chen, Yi-Chu
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01.07.2007
• Subjects: Exact sciences and technology; Fundamental areas of phenomenology (including applications); Measurement and testing methods; Physics; Solid mechanics; Structural and continuum mechanics; TECHNICAL PAPERS; Bayes estimation; Monte Carlo method; Averaging method; Probabilistic approach; Markov chains; Multimode waveguide; Modeling; Adaptive method; Markov chain; Model matching; Simulation; Probability density function; System identification; Bayesian analysis

This paper presents a newly developed simulation-based approach for Bayesian model updating, model class selection, and model averaging called the transitional Markov chain Monte Carlo (TMCMC) approach. The idea behind TMCMC is to avoid the problem of sampling from difficult target probability densi...