A measurement process has imperfections that give rise to uncertainty in each measurement result. Statistical tools give the assessment of uncertainties associated to the results only if all the relevant quantities involved in the process are interpreted or regarded as random variables. In other terms all the sources of uncertainty are characterized by probability distribution functions, the form of which is assumed to either be known from measurements or unknown and so conjectured. Entropy is an information measure associated with the probability distribution of any random variable, so that it plays an important role in the metrological activity. In this paper the author introduces two basic entropy optimization principles: the Jaynes’s principle of maximum entropy and the Kulback’s principle of minimum cross-entropy (minimum directed divergence) and discusses the methods to approach the optimal solution of those entropic forms in some specific measurements models.

Evaluation of measurement uncertainty with the principles of entropy optimization / Zanobini, A.;. - STAMPA. - (2008), pp. 1-6. ((Intervento presentato al convegno Advanced methods for uncertainty estimation in measurement, 2008. amuem 2008. ieee international workshop on tenutosi a Trento (Italy) nel July 2008 [10.1109/AMUEM.2008.4589925].

Evaluation of measurement uncertainty with the principles of entropy optimization

ZANOBINI, ANDREA
2008

Abstract

A measurement process has imperfections that give rise to uncertainty in each measurement result. Statistical tools give the assessment of uncertainties associated to the results only if all the relevant quantities involved in the process are interpreted or regarded as random variables. In other terms all the sources of uncertainty are characterized by probability distribution functions, the form of which is assumed to either be known from measurements or unknown and so conjectured. Entropy is an information measure associated with the probability distribution of any random variable, so that it plays an important role in the metrological activity. In this paper the author introduces two basic entropy optimization principles: the Jaynes’s principle of maximum entropy and the Kulback’s principle of minimum cross-entropy (minimum directed divergence) and discusses the methods to approach the optimal solution of those entropic forms in some specific measurements models.
AMUEM
Advanced methods for uncertainty estimation in measurement, 2008. amuem 2008. ieee international workshop on
Trento (Italy)
July 2008
Zanobini, A.;
File in questo prodotto:
File Dimensione Formato  
Evaluation of measurement uncertainty with the principles of entropy optimization.pdf

Accesso chiuso

Tipologia: Altro
Licenza: DRM non definito
Dimensione 430.85 kB
Formato Adobe PDF
430.85 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/362655
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact