Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging

By Jianye Ching, Yi-Chu Chen. 2007

819 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/93753c22102a4130f943dbe065c9d275b0e6fab7

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In Artificial Intelligence

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Citations in the corpus

Model selection in finite element model updating using the Bayesian evidence statistic (2011, methodology): “Other sampling techniques, for example TMCMC [7,29], that are based on the Monte Carlo Markov Chain (MCMC) paradigm can be used, but these tend to only sample the peaks of the posterior distribution which can under-sample most of the narrow best-approximation regions [19,27].”
Bayesian Updating with Structural Reliability Methods (2015, methodology): “In the applications presented in this paper, the number of model evaluations required for Bayesian updating is on the order of 10(3) − 10(4), which is similar to existing state-of-the-art methods such as transitional MCMC proposed in (Ching and Chen 2007) or the hybrid MCMC approach of (Cheung and B”
Bayesian system identification based on probability logic (2010, methodology): “Samples are generated from the posterior PDF p ð h j D ; M i Þ by using an MCMC (Markov chain Monte Carlo) method which is a hybrid algorithm based on TMCMC [22] and Hybrid MC [24,25].”
Accounting for prediction uncertainty when inferring subsurface fault slip (2014, methodology): “As proposed initially by Beck & Au (2002) and Ching & Chen (2007), the CATMIP algorithm samples from a series of intermediate PDFs: f (m, βi ) ∝ p(m) p(dobs|m) β , i = 1, . . . , B 0 = β0 β1 β2 . . . βB = 1.”
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, neutral): “This includes the employment of polynomial chaos expansions as forward model substitutes [62–64], advanced stochastic simulation techniques [65, 66] and forward model reduction methods [67, 68].”