Usikkerhedspropagering

Usikkerhedspropagering, also known as uncertainty propagation, is a mathematical technique used to quantify the uncertainty of a variable that is dependent on other variables. It is widely used in fields such as engineering, physics, and economics, where precise measurements and predictions are crucial. The technique involves propagating the uncertainties of input variables through a mathematical model to determine the uncertainty of the output variable.

The most common method of uncertainty propagation is the Monte Carlo method, which involves generating a large number of random samples of the input variables, each with a distribution that reflects its uncertainty. These samples are then passed through the mathematical model to produce a distribution of output values. The uncertainty of the output variable is then characterized by the spread of this distribution.

Another method of uncertainty propagation is the use of sensitivity analysis, which involves determining how the output variable changes in response to changes in the input variables. This can help identify which input variables have the greatest impact on the output variable and thus require the most precise measurements.

Usikkerhedspropagering is an important tool for ensuring the reliability and accuracy of measurements and predictions. By quantifying the uncertainty of a variable, it allows for more informed decision-making and can help identify areas where further research or measurement is needed. However, it is important to note that the accuracy of the uncertainty propagation depends on the accuracy of the input data and the mathematical model used. Therefore, it is crucial to carefully validate and verify both the input data and the model before using uncertainty propagation.