Lye, Adolphus 
ORCID: 0000-0002-1803-8344, Gray, Ander ORCID: 0000-0002-1585-0900 and Patelli, Edoardo ORCID: 0000-0002-5007-7247
(2021)
Identification of Time-varying Parameters using Variational Bayes -- Sequential Ensemble Monte Carlo Sampler.
In: Proceedings of the 31st European Safety and Reliability Conference, 2021-9-19 - 2021-9-23, Angers, France.
|
Text
ESREL 2021 Conference Paper (Final submission).pdf - Author Accepted Manuscript Download (515kB) | Preview |
Abstract
This work presents an extended sequential Monte Carlo sampling algorithm embedded with a Variational Bayes step. The algorithm is applied to estimate the distribution of time-varying parameters in a Bayesian filtering procedure. This algorithm seeks to address the case whereby the state-evolution model does not have an inverse function. In the proposed approach, a Gaussian mixture model is adopted whose covariance matrix is determined via principle component analysis. As a form of verification, a numerical example involving the identification of inter-storey stiffness within a 2-DOF shear building model is presented whereby the stiffness parameters degrade according to a simple State-evolution model whose inverse function can be derived. The Variational Bayes-sequential ensemble Monte Carlo sampler is implemented alongside the Sequential Monte Carlo sampler and the results compared on the basis of the accuracy and precision of the estimates as well computational time. A non-linear time-series model whose state-evolution model does not yield an inverse function is also analysed to show the applicability of the proposed approach.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Variational Bayes, Bayesian Model Updating, Sequential Monte Carlo, Uncertainty Quantification, Gaussian Mixture Model, Markov Model |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Jun 2021 08:01 |
| Last Modified: | 18 Jan 2023 22:35 |
| DOI: | 10.3850/978-981-18-2016-8_081-cd |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3125874 |
Dimensions
Dimensions
