A fast Bayesian approach using adaptive densifying approximation technique accelerated MCMC. Issue 2 (12th February 2016)

• Record Type: Journal Article
• Authors: Zhang, W.; Liu, J.; Cho, C.; Han, X.
• Is Part Of: Inverse problems in science and engineering. Volume 24:Issue 2(2016)
• Page Start: 247
• Page End: 264
• Publication Date: 2016-02-12
• Subjects: inverse problems -- parameter identification -- fast Bayesian approach -- radial basis functions (RBF) -- adaptive densifying approximation -- Markov Chain Monte Carlo (MCMC); 62F15; Engineering mathematics -- Periodicals; Inverse problems (Differential equations) -- Periodicals; 620.001515357
• DOI: 10.1080/17415977.2015.1017488
• Languages: English
• ISSNs: 1741-5977

Abstract

Abstract : A novel algorithm is presented in this study to improve the efficiency and accuracy of Bayesian approach for fast sampling of posterior distributions of the unknown structure parameters. This algorithm can save a computational cost by resolving the efficiency problem in Bayesian identifications. In this algorithm, an approximation model based on the radial basis function is first used to replace the actual joint posterior distribution of the unknown parameters. An adaptive densifying technique is then introduced to increase the accuracy of the approximation model by reconstructing them with densified samples. Finally, the marginal posterior distributions for each parameter with fine accuracy can be efficiently achieved using the Markov Chain Monte Carlo method based on the present densified approximation model. Two numerical examples are investigated to demonstrate that the present algorithm can achieve significant computational gains without sacrificing the accuracy.