Overcoming the Artificial Biases for the Nonadditive $ q $-Entropy

Abstract

Entropy maximization procedure has been a general practice in many diverse fields of science to obtain the concomitant probability distributions. The consistent use of the maximization procedure on the other hand requires the probability distributions to obey the probability multiplication rule for independent events. However, despite that the nonadditive $ q $-entropy is known not to obey this rule, it is still used with the entropy maximization procedure to infer the probability distributions at the expense of creating artificial biases not present in the data itself. Here we show that this important obstacle can be overcome by considering the intrinsic discrete structure and related averaging scheme of the nonadditive $ q $-entropy. This also paves the road to a better understanding of the entropy maximization procedure of Jaynes.