## 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 language | English |
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Pages (from-to) | 156-162 |

Journal | NUCLEAR INSTRUMENTS AND METHODS IN PHYSICS RESEARCH SECTION A: ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT |

Volume | 854 |

DOIs | |

Publication status | Published - 11 May 2017 |

MoE publication type | A1 Journal article-refereed |

## Keywords

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