Unknown

Dataset Information

0

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty.


ABSTRACT: In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories.

SUBMITTER: Green PL 

PROVIDER: S-EPMC4549940 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC10564381 | biostudies-literature
| S-EPMC3464018 | biostudies-literature
| S-EPMC6760159 | biostudies-literature
| S-EPMC4072599 | biostudies-literature
| S-EPMC5482548 | biostudies-literature
| S-EPMC5482939 | biostudies-literature
| S-EPMC2807240 | biostudies-literature
| S-EPMC7224357 | biostudies-literature
| S-EPMC5354282 | biostudies-literature
| S-EPMC6691032 | biostudies-literature