Green, PL
(2015)
Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution.
PROBABILISTIC ENGINEERING MECHANICS, 42.
pp. 54-63.
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Abstract
In the last 20 years the applicability of Bayesian inference to the system identification of structurally dynamical systems has been helped considerably by the emergence of Markov chain Monte Carlo (MCMC) algorithms - stochastic simulation methods which alleviate the need to evaluate the intractable integrals which often arise during Bayesian analysis. In this paper specific attention is given to the situation where, with the aim of performing Bayesian system identification, one is presented with very large sets of training data. Building on previous work by the author, an MCMC algorithm is presented which, through combing Data Annealing with the concept of ‘highly informative training data’, can be used to analyse large sets of data in a computationally cheap manner. The new algorithm is called Smooth Data Annealing.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Nonlinear system identification, Markov chain Monte Carlo, Bayesian inference, Smooth data annealing, Big data |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Sep 2015 09:21 |
| Last Modified: | 15 Dec 2022 16:18 |
| DOI: | 10.1016/j.probengmech.2015.09.010 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/2028419 |
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