propagation of uncertainty

For the propagation of uncertainty through time, see Chaos theory.

The more sophisticated scientific rules are known as propagation of uncertainty.

See the article on propagation of uncertainty for these more advanced and precise rules.

This article deals with the propagation of uncertainty via algebraic manipulations.

A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty.

Significance arithmetic is a set of rules (sometimes called significant figure rules) for approximating the propagation of uncertainty in scientific or statistical calculations.

For example, the observational error in an experiment is usually assumed to follow a normal distribution, and the propagation of uncertainty is computed using this assumption.

In statistics, propagation of uncertainty (or propagation of error) is the effect of variables' uncertainties (or errors) on the uncertainty of a function based on them.

Calibration/Interpolation: Errors that occur due to calibration uncertainty at the cal points, or between cal point due to propagation of uncertainty or curve fit errors.

Because of that the results of the interval finite element (or in general worst case analysis) may be overestimated in comparison to the stochastic fem analysis (see also propagation of uncertainty).

There are two major types of problems in uncertainty quantification: one is the forward propagation of uncertainty and the other is the inverse assessment of model uncertainty and parameter uncertainty.

Another reason is that a large number of results and methods (such as propagation of uncertainty and least squares parameter fitting) can be derived analytically, in explicit form, when the relevant variables are normally distributed.

The output quantities are now described by a joint probability distribution, the coverage interval becomes a coverage region, the law of propagation of uncertainty has a natural generalization, and a calculation procedure that implements a multivariate Monte Carlo method is available.