izpis_h1_title_alt

Experimental information and statistical modeling of physical laws
Grabec, Igor (Avtor)

URLURL - Predstavitvena datoteka, za dostop obiščite http://arxiv.org/abs/cs/0612027 Novo okno

Izvleček
Statistical modeling of physical laws connects experiments with mathematical descriptions of natural phenomena. The modeling is based on the probability density of measured variables expressed by experimental data via a kernel estimator. As an objective kernel the scattering function determined by calibration of the instrument is introduced. This function provides for a new definition of experimental information and redundancy of experimentation in terms of information entropy. The redundancy increases with the number of experiments, while the experimental information converges to a value that describes the complexity of the data. The difference between the redundancy and the experimental information is proposed as the model cost function. From its minimum, a proper number of data in the model is estimated. As an optimal,nonparametric estimator of the relation between measured variables theconditional average extracted from the kernel estimator is proposed. The modeling is demonstrated on noisy chaotic data.

Jezik:Angleški jezik
Ključne besede:fizika, fizikalni zakoni, statistično modeliranje, neparametrične regresije, redundanca, kernel estimator, experimental information, complexity, redundancy, modeling, physical law, nonparametric regression, predictor quality, noisy chaotic generator
Vrsta gradiva:Delo ni kategorizirano (r6)
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2006
Št. strani:5 f.
UDK:53
COBISS.SI-ID:10030619 Povezava se odpre v novem oknu
Število ogledov:288
Število prenosov:171
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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