ON THE USE OF MINIMUM CROSS ENTROPY PRINCIPLE AND BAYES’ THEOREM FOR THE UNCERTAINTY EVALUATION IN A MEASUREMENT PROCESS |
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| G. Iuculano, G. Pellegrini, A. Zanobini |
- Abstract:
- In this paper the evaluation of measurement uncertainty in a multivariate model is carried out by applying the principle of minimum cross entropy (MINCENT) and Bayes’ theorem.
In particular the MINCENT optimization procedure is used to translate the information contained in the known form of likelihood into a prior distribution for Bayesian inference. The methodology is adapted and tested on a recalibration model. Some basic ideas and general remarks on the Bayesian probability theory and entropy optimization principles are reported too. - Keywords:
- Measurement Uncertainty, Bayesian Inference, Minimum cross Entropy
- Download:
- PWC-2006-TC21-008u.pdf
- DOI:
- -
- Event details
- Event name:
- XVIII IMEKO World Congress
- Title:
Metrology for a Sustainable Development
- Place:
- Rio de Janeiro, BRAZIL
- Time:
- 17 September 2006 - 22 September 2006