Randomized Monte Carlo Algorithms for Problems with Random Parameters (“Double Randomization” Method)

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Randomized Monte Carlo algorithms are constructed by a combination of a basic probabilistic model and its random parameters to investigate parametric distributions of linear functionals. An optimization of the algorithms with a statistical kernel estimator for the probability density is presented. A randomized projection algorithm for estimating a nonlinear functional distribution is formulated and applied to the investigation of the criticality fluctuations of a particle multiplication process in a random medium.

Original languageEnglish
Pages (from-to)155-165
Number of pages11
JournalNumerical Analysis and Applications
Volume12
Issue number2
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • double randomization method
  • probabilistic model
  • random medium
  • random parameter
  • randomized algorithm
  • splitting method
  • statistical kernel estimator
  • statistical modeling

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