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Uncertainty Quantification for Seismic Risk Assessment using Latin Hypercube Sampling and Quasi Monte Carlo Simulation

Scheingraber, Christoph, and Martin Käser (2018), Uncertainty Quantification for Seismic Risk Assessment using Latin Hypercube Sampling and Quasi Monte Carlo Simulation, in Proceedings of the 16th European Conference on Earthquake Engineering.

Abstract
In the insurance industry Seismic Risk Assessment is commonly used for modeling loss to a spatially distributed portfolio. Best practice not only involves the computation of expected loss, but also requires treatment of the uncertainty of all components of the modeling chain. Because the dimensionality is high, this is typically performed with a Monte Carlo simulation of a large number of scenario realizations. In this study, we first compare the computational efficiency of uncorrelated pseudo-random sampling to variance reduction techniques for scenario loss uncertainty treatment. We observe that Latin Hypercube sampling as well as Quasi Monte Carlo simulation using low-discrepancy sequences can improve the error convergence.....
BibTeX
@inproceedings{id2349,
  author = {Christoph Scheingraber and Martin K{\"a}ser},
  booktitle = {Proceedings of the 16th European Conference on Earthquake  Engineering},
  title = {{Uncertainty Quantification for Seismic Risk Assessment using Latin Hypercube Sampling and Quasi Monte Carlo Simulation}},
  year = {2018},
}
EndNote
%A Scheingraber, Christoph
%A Käser, Martin
%D 2018
%T Uncertainty Quantification for Seismic Risk Assessment using Latin Hypercube Sampling and Quasi Monte Carlo Simulation
%B Proceedings of the 16th European Conference on Earthquake  Engineering
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Printed 17. Aug 2019 12:38