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Itô-SDE MCMC method for Bayesian characterization of errors associated with data limitations in stochastic expansion methods for uncertainty quantification
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References

  • 2010Partial Differential Equations827 cited
  • 2010Spectral methods for uncertainty quantification : with applications to computational fluid dynamics572 cited
  • 2006Stochastic spectral methods for efficient Bayesian solution of inverse problems471 cited
  • 2006On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data191 cited
  • 2008Construction of probability distributions in high dimension using the maximum entropy principle: Applications to stochastic processes, random fields and random matrices117 cited
  • 2010Identification of Bayesian posteriors for coefficients of chaos expansions103 cited
  • 2015Polynomial Chaos Expansion of a Multimodal Random Vector54 cited
  • 2012Time-domain formulation in computational dynamics for linear viscoelastic media with model uncertainties and stochastic excitation49 cited
  • 2014AN OVERVIEW OF NONINTRUSIVE CHARACTERIZATION, PROPAGATION, AND SENSITIVITY ANALYSIS OF UNCERTAINTIES IN COMPUTATIONAL MECHANICS31 cited
  • 2014Itô SDE--based Generator for a Class of Non-Gaussian Vector-valued Random Fields in Uncertainty Quantification21 cited
  • 2017Uncertainty Quantification14 cited
  • 2015Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models10 cited

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