Improved probabilistic point estimation schemes for uncertainty analysis

Ying Wang, Yeou-Koung Tung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The probabilistic point estimation (PPE) methods replace the probability distribution of the random parameters of a model with a finite number of discrete points in sample space selected in such a way to preserve limit probabilistic information of involved random parameters. Most PPE methods developed thus far match the distribution of random parameters up to the third statistical moment and, in general, could provide reasonable accurate estimation only for the first two statistical moments of model output. This study proposes two optimization-based point selection schemes for the PPE methods to enhance the accuracy of higher-order statistical moments estimation for model output. Several test models of varying degrees of complexity and nonlinearity are used to examine the performance of the proposed point selection schemes. The results indicate that the proposed point selection schemes provide significantly more accurate estimation of model output uncertainty features than the existing schemes.

Original languageEnglish
Pages (from-to)1042-1057
Number of pages16
JournalApplied Mathematical Modelling
Issue number2
StatePublished - 1 Feb 2009


  • Optimization
  • Probabilistic point estimation methods
  • Uncertainty analysis

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