On the maximum entropy distributions of inherently positive nuclear data

A. Taavitsainen*, R. Vanhanen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

The multivariate log-normal distribution is used by many authors and statistical uncertainty propagation programs for inherently positive quantities. Sometimes it is claimed that the log-normal distribution results from the maximum entropy principle, if only means, covariances and inherent positiveness of quantities are known or assumed to be known. In this article we show that this is not true. Assuming a constant prior distribution, the maximum entropy distribution is in fact a truncated multivariate normal distribution – whenever it exists. However, its practical application to multidimensional cases is hindered by lack of a method to compute its location and scale parameters from means and covariances. Therefore, regardless of its theoretical disadvantage, use of other distributions seems to be a practical necessity.

Original languageEnglish
Pages (from-to)156-162
JournalNUCLEAR INSTRUMENTS AND METHODS IN PHYSICS RESEARCH SECTION A: ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Volume854
DOIs
Publication statusPublished - 11 May 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Log-normal multivariate distribution
  • Maximum entropy principle
  • Normal multivariate distribution
  • Nuclear data
  • Truncated multivariate normal distribution
  • Uncertainty propagation

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