Add to ORKGAbstract: 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
An important concern in the design of validation experiments is how to incorporate the mathematical ...
The presence of summarized statistical information, such as some statistics of the system response, ...
In the present communication entropy optimization principles namely maximum entropy principle and mi...
Abstract − A measurement process represents a controlled learning process in which various aspects o...
Based on the Bayesian principle, the modern uncertainty evaluation methods can fully integrate prior...
The principle of minimum cross-entropy is an inference procedure for specifying an updated probabili...
Typical statistical methods of data analysis only handle determinate uncertainty, the type of uncert...
ABSTRACT: In the present paper we will establish a fundamental result which has been used in the pri...
The concept of uncertain entropy is used to pro-vide a quantitative measurement of the uncertainty a...
Abstract: This paper presents an overview of minimum cross entropy method, which has been used exten...
AbstractThe main statistics used in rough set data analysis, the approximation quality, is of limite...
The importance of finding minimum entropy probability distributions and the value of minimum entropy...
When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be t...
Uncertainty is inevitable in product design processes. Therefore, to make reliable decisions, uncert...
The combination of mathematical models and uncertainty measures can be applied in the area of data m...
An important concern in the design of validation experiments is how to incorporate the mathematical ...
The presence of summarized statistical information, such as some statistics of the system response, ...
In the present communication entropy optimization principles namely maximum entropy principle and mi...
Abstract − A measurement process represents a controlled learning process in which various aspects o...
Based on the Bayesian principle, the modern uncertainty evaluation methods can fully integrate prior...
The principle of minimum cross-entropy is an inference procedure for specifying an updated probabili...
Typical statistical methods of data analysis only handle determinate uncertainty, the type of uncert...
ABSTRACT: In the present paper we will establish a fundamental result which has been used in the pri...
The concept of uncertain entropy is used to pro-vide a quantitative measurement of the uncertainty a...
Abstract: This paper presents an overview of minimum cross entropy method, which has been used exten...
AbstractThe main statistics used in rough set data analysis, the approximation quality, is of limite...
The importance of finding minimum entropy probability distributions and the value of minimum entropy...
When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be t...
Uncertainty is inevitable in product design processes. Therefore, to make reliable decisions, uncert...
The combination of mathematical models and uncertainty measures can be applied in the area of data m...
An important concern in the design of validation experiments is how to incorporate the mathematical ...
The presence of summarized statistical information, such as some statistics of the system response, ...
In the present communication entropy optimization principles namely maximum entropy principle and mi...