Performance Metrics, Error Modeling, and Uncertainty Quantification

Performance Metrics, Error Modeling, and Uncertainty Quantification

A common set of statistical metrics has been used to summarize the performance ofmodels ormeasurements— the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly de...

Performance Metrics , Error Modeling , and Uncertainty Quantification 3 4

Functional error modeling for uncertainty 1 quantification in hydrogeology

Bayesian network modeling metrics of performance and uncertainty

Model sensitivity analysis variance reduction ( ) ( ), where ( ) ∑ ( )[ ( )] , ( ) ∑ ( )[ ( )] , ( ) ∑ ( ) , where is the numeric real value of state q, ( ) is the expected real value of Q before applying new findings, ( ) is the expected real value of Q after applying new findings f for variable F, and ( ) is the variance of the real value of Q before any new findings [0,infinity], the greater...

Satellite Re-entry Modeling and Uncertainty Quantification

LEO trajectory modeling is a fundamental aerospace capability and has applications in many areas of aerospace, such as maneuver planning, sensor scheduling, re-entry prediction, collision avoidance, risk analysis, and formation flying. Somewhat surprisingly, modeling the trajectory of an object in low Earth orbit is still a challenging task. This is primarily due to the large uncertainty in the...