ENTROPY Procedure(Experimental)

Details: ENTROPY Procedure

Shannon’s measure of entropy for a distribution is given by

StartLayout 1st Row 1st Column normal m normal a normal x normal i normal m normal i normal z normal e 2nd Column Blank 3rd Column minus sigma-summation Underscript i equals 1 Overscript n Endscripts p Subscript i Baseline ln left-parenthesis p Subscript i Baseline right-parenthesis 2nd Row 1st Column normal s normal u normal b normal j normal e normal c normal t normal t normal o 2nd Column Blank 3rd Column sigma-summation Underscript i equals 1 Overscript n Endscripts p Subscript i Baseline equals 1 EndLayout

where p Subscript i is the probability associated with the ith support point. Properties that characterize the entropy measure are set forth by Kapur and Kesavan (1992).

The objective is to maximize the entropy of the distribution with respect to the probabilities p Subscript i and subject to constraints that reflect any other known information about the distribution (Jaynes 1957). This measure, in the absence of additional information, reaches a maximum when the probabilities are uniform. A distribution other than the uniform distribution arises from information already known.

Last updated: June 19, 2025