• Corpus ID: 220280239

Hamiltonian MCMC methods for estimating rare events probabilities in high-dimensional problems

@article{Papakonstantinou2020HamiltonianMM,
  title={Hamiltonian MCMC methods for estimating rare events probabilities in high-dimensional problems},
  author={Konstantinos Papakonstantinou and Hamed Nikbakht},
  journal={arXiv: Methodology},
  year={2020}
}
Accurate and efficient estimation of rare events probabilities is of significant importance, since often the occurrences of such events have widespread impacts. The focus in this work is on precisely quantifying these probabilities, often encountered in reliability analysis of complex engineering systems, based on an introduced framework termed Approximate Sampling Target with Post-processing Adjustment (ASTPA), which herein is integrated with and supported by gradient-based Hamiltonian Markov… 
1 Citations
A DIRECT HAMILTONIAN MCMC APPROACH FOR RELIABILITY ESTIMATION
  • Hamed Nikbakht, K. Papakonstantinou
  • Computer Science
    Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)
  • 2019
TLDR
This work introduces a gradient-based Hamiltonian Markov Chain Monte Carlo framework, termed Approximate Sampling Target with Post-processing Adjustment (ASTPA), to construct a relevant target distribution by weighting the high-dimensional random variable space through a one-dimensional likelihood model, using the limit-state function.

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  • Computer Science
    Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)
  • 2019
TLDR
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