Transitional markov chain monte carlo method for bayesian model updating

07 Dec

We demonstrate that the Markov chains of each subsample in TMCMC may result in uneven chain lengths that distort the intermediate target distributions and introduce bias accumulation in each stage of the TMCMC algorithm. We remedy this drawback of TMCMC by proposing uniform chain lengths, with or without burn-in, so that the algorithm emphasizes sequential importance sampling (SIS) over MCMC. , 2012, “ Bayesian Uncertainty Quantification and Propagation in Molecular Dynamics Simulations: A High Performance Computing Framework,” J. Functions like purchasing print books will be unavailable. Sign up for a free Git Hub account to open an issue and contact its maintainers and the community.Results of the study show that the present approach is an effective tool in system identification problem when only a few data is available for updation.

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Once it is fixed that which parameters are evaluated satisfactorily using the available modal data the remaining parameters are evaluated employing modal data of a virtual structure.

The convergence rate of the Hamiltonian/Hybrid Monte Carlo (HMC) algorithm is high due to its trajectory which is guided by the derivative of the posterior probability distribution function.

This can lead towards high probability areas in a reasonable period of time.

Still, in recent modelling efforts, roughness is usually treated as a static parameter or parametrized from literature, leading to strong simplification and data uncertainty in the description of these physical processes and the derivation of hydrological quantities.

However, this simplification is not only due to the lack of theoretical process knowledge, but rather refers to the lack of appropriate roughness input data, as it is very complex to measure roughness at field scale under natural conditions.