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

URLURL - Presentation file, Visit http://arxiv.org/abs/cs/0612027 This link opens in a new window

Abstract
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.

Language:English
Keywords:fizika, fizikalni zakoni, statistično modeliranje, neparametrične regresije, redundanca, kernel estimator, experimental information, complexity, redundancy, modeling, physical law, nonparametric regression, predictor quality, noisy chaotic generator
Work type:Not categorized (r6)
Organization:FS - Faculty of Mechanical Engineering
Year:2006
Number of pages:5 f.
UDC:53
COBISS.SI-ID:10030619 Link is opened in a new window
Views:547
Downloads:273
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