Introduction to the Information in Metric Space
Michèle Pappalardo · AIP conference proceedings · 2005
The idea of information, in the classic theories of Fisher and Wiener‐Shannon, is mutual or relative information only on probabilistic and repetitive events. The idea of information is larger than the probability. The Wiener‐Shannon’s axioms can be extended to the non‐probabilistic and repetitive events. It is possible to introduce a Theory of Information for events not connected to the probability. On the basis of so called Laplace’s Principle of insufficient knowledge, and from the MaxEnt Principle the MaxInf Principle is developed for choosing solutions in absence of knowledge. In this paper the information is applied in numeric analysis as method for to put in reciprocal relation data with polynomial functions.