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If solution errors or other sources of model discrepancy are likely to be important contributors to prediction uncertainty, their impact must also be captured in some way.

The available physical observations are key to any validation assessment.

For any prediction, assessing the quality, or reliability, of the prediction is crucial.

This concept of prediction reliability is more qualitative than is prediction uncertainty.

The bottom histogram shows residuals from the National Weather Service (NWS) forecast.

In some cases these data are observational, provided by nature (e.g., meteorological measurements, supernova luminosities); in other cases, data come from a carefully planned hierarchy of controlled experiments—e.g., the Predictive Engineering and Computational Sciences (PECOS) case study in Section 5.9.

In addition to physical observations, information may come from the literature or expert judgment that may incorporate historical data or known physical behavior.

The basic process includes identifying and representing key sources of uncertainty; identifying physical observations; experiments, or other information sources for the assessment; assessing prediction uncertainty; assessing the reliability or quality of the prediction; supplying information on how to improve the assessment; and communicating results.

Identifying and representing uncertainties typically involves sensitivity analysis to determine which features or inputs of the model affect key model outputs.

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