Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection

Markov Chain Monte Carlo (MCMC) simulation has a considerable computational burden when the target probability density function (PDF) evaluation involves a black-box, potentially computationally-expensive, numerical model. A novel framework to accelerate MCMC is developed here for such applications....

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Veröffentlicht in:	Computer methods in applied mechanics and engineering 2019-10, Vol.355, p.1124-1147
Hauptverfasser:	Zhang, Jize, Taflanidis, Alexandros A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms Adaptive Metropolis–Hastings AK-DRAM Approximation Bayesian analysis Computational efficiency Computer simulation Computing time Convergence Delayed rejection Efficiency Kriging Markov analysis Markov chain Monte Carlo Markov chains Mathematical analysis Model accuracy Numerical models Probability density functions Proposals Random walk Rejection Robustness (mathematics) Statistical inference Surrogate modeling
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