Uncertainty assessments based on observation and measurement equations
| Autor: | G. A. Kyriazis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Bayesian methods
Computer science Measurement uncertainty Measurement equation Transferability Process (computing) Contrast (statistics) Observation equation Electric apparatus and materials. Electric circuits. Electric networks Bayesian inference Industrial and Manufacturing Engineering Measurement equations Electronic Optical and Magnetic Materials Mechanics of Materials Electrical and Electronic Engineering TK452-454.4 Algorithm |
| Zdroj: | Measurement: Sensors, Vol 18, Iss, Pp 100075-(2021) |
| ISSN: | 2665-9174 |
| Popis: | At least two main approaches to the assessment of uncertainty in measurement have been proposed. One of them makes use of a measurement equation as stated in the GUM and its supplements to represent the model adopted for the measurement. The other approach, essentially the Bayesian inference, bases the assessment on an observation equation. It is shown here that the latter allows one to take prior knowledge about the measurand into account. It also allows measurement information to be used for updating knowledge about other non-observable quantitities. In addition, the transferability requirement of the GUM is satisfied. In contrast, the assessment based on a measurement equation hinders our learning process from measurement information. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|