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.

翻译：最近它被引入了对概率分布的否定。当基于知识的系统可以使用像不高(高)这样的术语时,这种否定的必要性就出现。例如,可以考虑高PROFIT或高PRICE。许多著作都考虑了在Dempster-Shafer中应用这种否定理论的问题。虽然已经提出了几处对概率分布的否定,但还不清楚如何构建其他否定。在本文中,我们把否认概率分布视为使用[0,1]所定义的不断减少的功能对pd进行点对点的转换。我们建议了生成纳热器和对pd的相应否定的一般方法,并研究了其特性。我们把线性纳热器定性为Yager和统一的纳热器的组合。