Trucano timothy g (2 Ergebnisse)
Description fo the Sandia Validation Metrics Project: SAND2001-1339
Trucano, Timothy G.; Easterling, Robert G.; Dowding, Kevin J.; Paez, Thomas L.; Urbina, Angel; Romero, VIcente J.; Rutherford, B.
Verlag: Sandia National Laboratories, Albuquerque, NM 2001
- Softcover
- Erstausgabe
Anbieter: Ground Zero Books, Ltd., Silver Spring, MD, USAGround Zero Books, Ltd.
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EUR 45,07
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Wraps. Presumed first edition/first printing. 77, [3] p. Includes illustrations. Some illustrations in color. References. This report described the underlying principles and goals of the sandia Accelerated Strategic Computing Initiative (ASCI) Verification and Validation Program Validation Metrics Project. It also gives a techni…cal description fo two case studies, one in structural dynamics and the aother in theromomechanics, that serve to focus the technical work of the prject in Fiscal Year 2001. Good. No dust jacket. Staple bound.
Verlag: Sandia National Laboratories, Albuquerque, NM 2002
- Softcover
- Erstausgabe
Anbieter: Ground Zero Books, Ltd., Silver Spring, MD, USAGround Zero Books, Ltd.
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht - Befriedigend
EUR 67,61
EUR 4,38 VersandVersand innerhalb von USAAnzahl: 1 verfügbar
Spiral bound. Zustand: Good. Presumed First Edition, First printing. 86, [2] pages. Tables. Figures. References. Minor page rippling at bottom edge. Two major issues associated with model validation are addressed here. First, we present a maximum likelihood approach to define and evaluate a model validation metric. The advantage… of this approach is it is more easily applied to nonlinear problems than the methods presented earlier by Hills and Trucano (1999, 2001); the method is based on optimization for which software packages are readily available; and the method can more easily be extended to handle measurement uncertainty and prediction uncertainty with different probability structures. Several examples are presented utilizing this metric. We show conditions under which this approach reduces to the approach developed previously by Hills and Trucano (2001). Secondly, we expand our earlier discussions (Hills and Trucano, 1999, 2001) on the impact of multivariate correlation and the effect of this on model validation metrics. We show that ignoring correlation in multivariate data can lead to misleading results, such as rejecting a good model when sufficient evidence to do so is not available.