Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling
Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not m...
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2007-01-01
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| Series: | Computational Intelligence and Neuroscience |
| Online Access: | http://dx.doi.org/10.1155/2007/71859 |
| Summary: | Bivariate statistical modeling from incomplete data is a useful statistical tool that
allows to discover the model underlying two data sets when the data in the two sets
do not correspond in size nor in ordering. Such situation may occur when the sizes of
the two data sets do not match (i.e., there are holes in the data) or when
the data sets have been acquired independently. Also, statistical modeling is useful when
the amount of available data is enough to show relevant statistical features of the
phenomenon underlying the data. We propose to tackle the problem of statistical
modeling via a neural (nonlinear) system that is able to match its input-output statistic to
the statistic of the available data sets. A key point of the new implementation proposed
here is that it is based on look-up-table (LUT) neural systems, which guarantee a
computationally advantageous way of implementing neural systems. A number of
numerical experiments, performed on both synthetic and real-world data sets, illustrate
the features of the proposed modeling procedure. |
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| ISSN: | 1687-5265 1687-5273 |