Experimental Information and Statistical Modeling of Physical Laws

Simple item page
dc.creatorGrabec, Igor
dc.date2006-12-05
dc.date.accessioned2026-07-07T08:16:51Z
dc.date.available2026-07-07T08:16:51Z
dc.descriptionStatistical 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 the conditional average extracted from the kernel estimator is proposed. The modeling is demonstrated on noisy chaotic data.
dc.descriptionan article submitted to "IEEE Transactions on Information Theory"
dc.identifierhttps://arxiv.org/abs/cs/0612027
dc.identifierhttp://arxiv.org/abs/cs/0612027
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133931
dc.subjectInformation Theory
dc.subjectInformation Retrieval
dc.titleExperimental Information and Statistical Modeling of Physical Laws
dc.typetext

Files

Collections

arXiv