Model Selection using Response Measurements: Bayesian Probabilistic Approach

By James L. Beck, Ka-Veng Yuen. 2004

620 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/eaf1ba3d2012e12381b9c975993f1aa57ecdbb5b

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In Civil and Structural Engineering

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Citations in the corpus

Bayesian Model Updating Using Hybrid Monte Carlo Simulation with Application to Structural Dynamic Models with Many Uncertain Parameters (2009, methodology): “TMCMC has several advantages over the previous approaches: 1 it is more efficient; 2 it allows the estimation of the normalizing constant c of p D ,M , which is important for Bayesian model class selection Beck and Yuen 2004 .”
Model selection in finite element model updating using the Bayesian evidence statistic (2011, background): “This is perhaps best approached using Bayesian inference, which allows one to deal with models in an intuitive and systematic way [3,4,17,19,20].”
Bayesian system identification based on probability logic (2010, methodology): “Laplace’s method of asymptotic approximation can be used when the model class is globally identiﬁable based on the available data D (e.g. [26,29]), which, in effect, utilizes a Gaussian PDF centered on the MLE (or MAP estimate) ^ h as an approximation to the posterior PDF p ð h j D ; M j Þ : where…”
Bayesian State Estimation Method for Nonlinear Systems and Its Application to Recorded Seismic Response (2006, methodology): “A Bayesian approach to model class selection for dynamical systems is given by Beck and Yuen 2004 .”
A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites (2010, methodology): “, [13]), relied on asymptotics in order to solve for the marginal parameter distributions which, as will be shown, often take the form of high-dimensional integrals.”
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, neutral): “Moreover one could treat the form of the forward model M itself as uncertain/random [45, 46] and select the most plausible class via Bayesian model selection [47, 48].”