MONTE CARLO PARAMETRIC IMPORTANCE SAMPLING WITH PARTICLE TRACKS SCALING.

Research output: Contribution to journalArticlepeer-review

Abstract

A method for Monte Carlo importance sampling with parametric dependence is proposed. The method depends on scaling of the particle tracks for a certain number of generated histories, to determine the position of the optimal biasing parameters. Computational results for a model problem show that difficulties pointed out by previous authors as to the occurrence of infinite variances or under-sampling can be easily understood and spotted by the careful user, and eliminated so as to obtain dependable results with moderate numbers of particle histories.

Original languageEnglish (US)
Pages (from-to)198-206
Number of pages9
JournalAtomkernenergie, Kerntechnik
Volume39
Issue number3
StatePublished - Jan 1 1981
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)

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