Statistical Validation of Engineering and Scientific Models: A Maximum Likelihood Based Metric; SAND2001-1783

Editore: Sandia National Laboratories, Albuquerque, NM, 2002

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Da: Ground Zero Books, Ltd., Silver Spring, MD, U.S.A.Ground Zero Books, Ltd.

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Descrizione dell’articolo da parte del venditore

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.…

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Dati bibliografici

Titolo
Statistical Validation of Engineering and Scientific Models: A Maximum Likelihood Based Metric; SAND2001-1783
Autore
Hills, Richard G. and Trucano, Timothy G.
Editore
Sandia National Laboratories, Albuquerque, NM
Anno di pubblicazione
2002
Condizione
Good
Rilegatura
Spiral bound
Edizione
Presumed First Edition, First printing.

Ground Zero Books, Ltd.

Silver Spring, MD, U.S.A.

Venditore con 5 stelle

Venditore AbeBooks dal 14 agosto 1998

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