On Sat, Apr 4, 2015 at 6:06 PM, Jon Awbrey <jawb...@att.net> wrote: > From a mathematical point of view, an "entropy" or "uncertainty" measure is > simply a measure on distributions that achieves its maximum when the > distribution is uniform. It is thus a measure of dispersion or uniformity. > > Measures like these can be applied to distributions that arise in any given > domain of phenomena, in which case they have various specialized meanings > and implications. > > When it comes to applications in communication and inquiry, the information > of a sign or message is measured by its power to reduce uncertainty. > > The following essay may be useful to some listers: > > http://intersci.ss.uci.edu/wiki/index.php/Semiotic_Information
Adding to the discussion "entropy" has been extended to neighbourhood systems, granulations with the intent of capturing roughness and information uncertainty in rough set theory. There are extensions to fuzzy sets as well. These measures essentially contribute to specific perspectives of understanding the ontology of information semantics relative the systems The measures implicitly assume a frequentist position - the probabilistic connections are not good enough. When fuzzy granulations are used, then the interpretation (by analogy with probabilistic idealisation) breaks down further. Regards A. Mani Prof(Miss) A. Mani CU, ASL, AMS, ISRS, CLC, CMS HomePage: http://www.logicamani.in Blog: http://logicamani.blogspot.in/ http://about.me/logicamani sip:girlprofes...@ekiga.net
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