Sequential Ensemble Monte Carlo Sampler for On-Line Bayesian Inference of Time-Varying Parameter in Engineering Applications

Lye, Adolphus ORCID: 0000-0002-1803-8344, Marino, Luca, Cicirello, Alice and Patelli, Edoardo ORCID: 0000-0002-5007-7247 (2023) Sequential Ensemble Monte Carlo Sampler for On-Line Bayesian Inference of Time-Varying Parameter in Engineering Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 9 (3). pp. 1-13.

Text
Final_PDF_File.pdf - Author Accepted Manuscript
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1115/1.4056934

Abstract

<jats:title>Abstract</jats:title> <jats:p>Several on-line identification approaches have been proposed to identify parameters and evolution models of engineering systems and structures when sequential datasets are available via Bayesian inference. In this work, a robust and “tune-free” sampler is proposed to extend one of the sequential Monte Carlo implementations for the identification of time-varying parameters which can be assumed constant within each set of data collected but might vary across different sequences of datasets. The proposed approach involves the implementation of the affine-invariant Ensemble sampler in place of the Metropolis–Hastings sampler to update the samples. An adaptive-tuning algorithm is also proposed to automatically tune the step-size of the affine-invariant ensemble sampler which, in turn, controls the acceptance rate of the samples across iterations. Furthermore, a numerical investigation behind the existence of inherent lower and upper bounds on the acceptance rate, making the algorithm robust by design, is also conducted. The proposed method allows for the off-line and on-line identification of the most probable models under uncertainty. The proposed sampling strategy is first verified against the existing sequential Monte Carlo sampler in a numerical example. Then, it is validated by identifying the time-varying parameters and the most probable model of a nonlinear dynamical system using experimental data.</jats:p>

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	09 Jan 2023 09:37
Last Modified:	13 Nov 2023 12:12
DOI:	10.1115/1.4056934
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166850