  Recently it was introduced a negation of a probability distribution. The need
for such negation arises when a knowledge-based system can use the terms like
NOT HIGH, where HIGH is represented by a probability distribution (pd). For
example, HIGH PROFIT or HIGH PRICE can be considered. The application of this
negation in Dempster-Shafer theory was considered in many works. Although
several negations of probability distributions have been proposed, it was not
clear how to construct other negations. In this paper, we consider negations of
probability distributions as point-by-point transformations of pd using
decreasing functions defined on [0,1] called negators. We propose the general
method of generation of negators and corresponding negations of pd, and study
their properties. We give a characterization of linear negators as a convex
combination of Yager and uniform negators.
