Florida International University
Edit Your Profile
FIU Discovery
Toggle navigation
Browse
Home
People
Organizations
Scholarly & Creative Works
Research Facilities
Support
Edit Your Profile
Why k-systems methodology works
Article
Trivedi, S, Jones, B, Iyengar, S. (2002). Why k-systems methodology works .
42(1), 23-31. 10.1080/02329290212170
Share this citation
Twitter
Email
Trivedi, S, Jones, B, Iyengar, S. (2002). Why k-systems methodology works .
42(1), 23-31. 10.1080/02329290212170
Copy Citation
Share
Overview
Identifiers
Additional Document Info
View All
Overview
cited authors
Trivedi, S; Jones, B; Iyengar, S
authors
Iyengar, S.S.
abstract
k-Systems methodology takes an arbitrary finite valued multivariate real function and transforms it into a dimensionless (0,1) interval function with associated "marginal" equations. Computations are then performed on this function and its marginal equations invoking solely the principle of maximum entropy. Many traditional statistical measures are thereby computed in this context. The claim is that this constitutes a correct framework for these measures. This theory has many statisticians throwing up their hands in consternation. The principle of maximum entropy is somewhat mysterious itself, but now the k-systems (0,1) function no longer represents relative frequencies, and the principle of maximum entropy has only been applied and rationalized for relative frequencies. Puzzling questions arise: Just what is this methodology doing, why does it seem to do it so well, and why and when is it correct? In this paper we explore the rationale behind k-systems. © 2002 Taylor & Francis Ltd.
publication date
January 1, 2002
Identifiers
Digital Object Identifier (DOI)
https://doi.org/10.1080/02329290212170
Additional Document Info
start page
23
end page
31
volume
42
issue
1