NDLI: Mathematical modeling using partition theory and data compression

Content Provider	IEEE Xplore Digital Library
Author	Newton, D.C.
Copyright Year	1996
Description	Author affiliation: 986 Cobblestone Drive, Orange Park, FL, USA (Newton, D.C.)
Abstract	This paper provides additional insight and information about a newly described method and mathematical model which is created by using number theory and a lossless data compression technique. The central tenet of this method and model suggests a strong connection between additive number theory (partitions) and statistics-in that, it shows how a scatterplot or a set of data, if modeled as a finite binary string, can be 'classified' using partition theory. Secondly, this method contributes the idea of using partitions as a model from which objective probabilities are derived-thus, linking the concept of statistics with data compression via partition theory. In the context of this method and model, a third important idea emerges-in which, a set of data or a scatterplot is converted into a unique real number (called a CADAMA number) which can be plotted and used for the purpose of storing and retrieving the original set of data, data analysis, and can potentially enhance or guide the decision-making process of a decision-maker. Finally, this method and model provides an opportunity to further validate, reexamine, and refine some of the fundamental first principles in statistics from a number theoretic point of view.
Starting Page	530
Ending Page	535
File Size	497720
Page Count	6
File Format	PDF
ISBN	0780332687
ISSN	10878785
DOI	10.1109/SOUTHC.1996.535121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-25
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Data compression Scattering Statistics Probability Joining processes Context modeling Information retrieval Data analysis Decision making
Content Type	Text
Resource Type	Article

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