@inproceedings{644c2ad6c0d34f89874bdb5cfb2cc03d,
title = "An adaptive markov Chain monte carlo method for Bayesian finite element model updating",
abstract = "In this paper, an adaptive Markov Chain Monte Carlo (MCMC) approach for Bayesian finite element model updating is presented. This approach is known as the Adaptive Hamiltonian Monte Carlo (AHMC) approach. The convergence rate of the Hamiltonian/Hybrid Monte Carlo (HMC) algorithm is high due to its trajectory which is guided by the derivative of the posterior probability distribution function. This can lead towards high probability areas in a reasonable period of time. However, the HMC performance decreases when sampling from posterior functions of high dimension and when there are strong correlations between the uncertain parameters. The AHMC approach, a locally adaptive version of the HMC approach, allows efficient sampling from complex posterior distribution functions and in high dimensions. The efficiency and accuracy of the AHMC method are investigated by updating a real structure.",
keywords = "Adaptive, Bayesian, Finite element model updating, Hybrid Monte Carlo, Markov Chain Monte Carlo",
author = "I. Boulkaibet and T. Marwala and Friswell, {M. I.} and S. Adhikari",
note = "Publisher Copyright: {\textcopyright} The Society for Experimental Mechanics, Inc. 2016.; 34th IMAC, Conference and Exposition on Structural Dynamics, 2016 ; Conference date: 25-01-2016 Through 28-01-2016",
year = "2016",
doi = "10.1007/978-3-319-29910-5_6",
language = "English",
isbn = "9783319299099",
series = "Conference Proceedings of the Society for Experimental Mechanics Series",
publisher = "Springer New York LLC",
pages = "55--65",
editor = "Tarazaga, {Pablo A.} and Paolo Castellini and {di Miao}, Dario",
booktitle = "Special Topics in Structural Dynamics - Proceedings of the 34th IMAC, A Conference and Exposition on Structural Dynamics 2016",
}