| Status | 已發表Published |
| Bayesian updating of nonlinear model predictions using Markov chain Monte Carlo simulation | |
| Beck J.L.; Au S.K.; Yuen K.-V. | |
| 2001-12-01 | |
| Source Publication | Proceedings of the ASME Design Engineering Technical Conference
 |
| Volume | 6 A |
| Pages | 821-828 |
| Abstract | The usual practice in system identification is to use system data to identify one model from a set of possible models and then to use this model for predicting system behavior. In contrast, the present robust predictive approach rigorously combines the predictions of all the possible models, appropriately weighted by their updated probabilities based on the data. This Bayesian system identification approach is applied to update the robust reliability of a dynamical system based on its measured response time histories. A Markov chain simulation method based on the Metropolis-Hastings algorithm and an adaptive scheme is proposed to evaluate the robust reliability integrals. An example for updating the reliability of a Duffing oscillator is given to illustrate the proposed method. |
| URL | View the original |
| Language | 英語English |
| Fulltext Access | |
| Document Type | Conference paper |
| Collection | University of Macau |
| Affiliation | California Institute of Technology |
| Recommended Citation GB/T 7714 | Beck J.L.,Au S.K.,Yuen K.-V.. Bayesian updating of nonlinear model predictions using Markov chain Monte Carlo simulation[C], 2001, 821-828. |
| APA | Beck J.L.., Au S.K.., & Yuen K.-V. (2001). Bayesian updating of nonlinear model predictions using Markov chain Monte Carlo simulation. Proceedings of the ASME Design Engineering Technical Conference, 6 A, 821-828. |
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