Bayesian inference on the parameters of the truncated normal distribution and application to reverberation chamber measurement data

Ramiro Serra (Corresponding author), Carlo Carobbi

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Assessing the performance of reverberation chambers (RCs) is a continuous problem which requires constant development and analysis. Several performance indicators exist, have been proposed and continue to be proposed in the literature. Each one of these indicators exhibits different levels of applicability, robustness or relevance towards a particular aspect of the field (statistical) behavior inside the RC. In this paper, we address this problem by introducing an alternative methodology able to estimate the distribution of the statistical parameters of interest. The methodology is based on Bayesian inference and it is applied to two different statistical models for RC measurement data and to two different loading conditions. The present paper is a significant expansion of a relevant abstract presented at the 'Mathematical and Statistical Methods for Metrology' Workshop, Torino, Italy, 30-31 May 2019. The corresponding abstract can be found at http://www.msmm2019.polito.it/programme.

Original languageEnglish
Article number074003
Number of pages13
JournalMeasurement Science and Technology
Volume31
Issue number7
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Bayesian inference
  • reverberation chamber
  • truncated normal probability density function

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