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A general approach to statistical modeling of physical laws : nonparametric regression
Grabec, Igor (Avtor)

URLURL - Predstavitvena datoteka, za dostop obiščite http://arxiv.org/abs/0704.0089 Povezava se odpre v novem oknu

Izvleček
Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup.A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance betweendata points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.

Jezik:Angleški jezik
Ključne besede:fizika, fizikalni zakoni, statistično modeliranje, neparametrične regresije, kernel estimator, experimental information, complexity, redundancy, modeling, physical law, nonparametric regression
Vrsta gradiva:Delo ni kategorizirano (r6)
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2007
Št. strani:17 f.
UDK:53
COBISS.SI-ID:10030875 Povezava se odpre v novem oknu
Število ogledov:463
Število prenosov:168
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