Information Measures Based on Sampling Distributions
Information theory and Statistics play an important role in Biological Sciences when we use information measures for the study of diversity and equitability. In this communication, we develop the link among the three disciplines and prove that sampling distributions can be used to develop new information measures. Our study will be an interdisciplinary and will find its applications in Biological systems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058933Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1076
 K. McGarigal and B.J. Marks, 1995. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. General Technical Report (USDA Forestry Service, Corvallis, 1995).
 A. Chawla, S. Rajkumar, K.N. Singh, Brij Lal, R.D. Singh and A.K. Thukral, Plant species diversity along an altitudinal gradient of Bhabha valley in western Himalaya. Journal of Mountain Science 5 (2008) 157- 177.
 A.E. Magurran, Ecological Diversity and its Measurement (Croom Helm, 1988, London).
 M, Gadgil and Meher-Homji, V.M. Ecological diversity. In: J.C. Daniel and J.S. Serrao (Eds.) Proceedings of the Centenary Seminar of the Bombay Natural History Society (Oxford University Press, Bombay, 1990).
 S.Baumgartner, Measuring Biodiversity (University of Heidelberg, Heidelberg, 2005).
 P.D.S. Kinako, Mathematical elegance and ecological naivety of diversity indices, African Journal of Ecology 21 (2009) 93-99.
 O.Parkash and A.K.Thukral, Statistical measures as measures of diversity, International Journal of Biomathematics 2009 (In press).
 J.H. Havrada and F.Charvat, Quantification methods of classification process:Concept of structural -entropy, Kybernetika 3, (1967) 30-35.
 B. H. Lavenda, Mean Entropies, Open System Infor. Dyn. 12, (2005) 289-302.
 A.K. Nanda. and P. Paul, Some results on generalized residual entropy, Information Sciences 176 (2006) 27-47.
 O. Onicescu, Energie Informationelle (C.R.Academic Science, 1966).
 O. Parkash, P. K. Sharma and R. Mahajan, New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle, Information Sciences 178 (2008) 2389-2395.
 O. Parkash, P. K. Sharma and R. Mahajan, Optimization principle for weighted fuzzy entropy using unequal constraints, Southeast Asian Bulletin Mathematics 2008 (In Press).
 M.C. Rao, V.B.C.,Yunmei and F.Wang, Commulative residual entropy: a new measure of Information, IEEE Trans. Inform. Theory 50 (2004) 1220-1228.
 A. Renyi , On measures of entropy and information, Proc. 4th Ber. Symp. Math. Stat. and Prob. 1 (1961) 547-561.
 C. E. Shannon, A mathematical theory of communication, Bell. Sys. Tech. Jr. 27(1948) 379-423, 623-659.
 E. H. Simpson, Measurement of diversity, Nature 163 (1949) 688.
 K. Zyczkowski, Renyi extrapolation of Shannon entropy, Open Syst. Inf. Dyn., 10 (1948) 297-310.