Daniela Tiboaca

Daniela Tiboaca
The University of Sheffield | Sheffield · Department of Mechanical Engineering

BEng Aerospace Engineering

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6
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Introduction
Working in Nonlinear SID within a Bayesian framework using MCMC sampling methods
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Publications

Publications (6)
Chapter
While system identification of linear systems is largely an established body of work encoded in a number of key references (including textbooks), nonlinear system identification remains a difficult problem and tends to rely on a “toolbox” of methods with no generally accepted canonical approach. Fairly recently, methods of parameter estimation usin...
Chapter
This paper is concerned with applying the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm on an MDOF system, within a Bayesian framework, in order to identify its parameters and do model selection simultaneously. Bayesian Inference has been widely used in the area of System Identification (SID) on issues of parameter estimation as well...
Conference Paper
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This study investigates the application of an advanced probabilistic method-Reversible Jump Markov Chain Monte Carlo (RJMCMC)-to the problem of locating, characterizing and assessing structural damage. A great deal of interest has been paid to treating the damage identification task as one of model updating with both deterministic and non-determini...
Chapter
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Validation approaches can determine the degree of accuracy of simulated models representing real structures. Therefore these approaches should deal with concepts concerning fidelity-to-data, the uncertainty quantification and the comparative metrics i.e. measures that quantify the level of agreement between simulation and experimental outcomes. In...
Conference Paper
Full-text available
The aim of this paper is to demonstrate the potential of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm when applied to system identification problems which involve both parameter estima-tion and model selection. Within the context of Bayesian Inference, Markov Chain Monte Carlo (MCMC) methods have been used for a long period of ti...
Conference Paper
Full-text available
The purpose of this contribution is to illustrate the potential of Reversible Jump Markov Chain Monte Carlo (RJMCMC) methods for nonlinear system identi�fication. Markov Chain Monte Carlo (MCMC) sampling methods have come to be viewed as a standard tool for tackling the issue of parameter estimation using Bayesian inference. A limitation of standar...

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