Open AccessAn Alternative Proof For the Minimum Fisher Information of Gaussian Distribution
Author(s) -
Abbas Pak
Publication year - 2018
Publication title -
journal of applied mathematics, statistics and informatics
Language(s) - English
Resource type - Journals
eISSN - 1339-0015
pISSN - 1336-9180
DOI - 10.2478/jamsi-2018-0008
Subject(s) - fisher information , gaussian , variance (accounting) , computer science , simple (philosophy) , information theory , set (abstract data type) , mathematics , key (lock) , algorithm , statistics , physics , philosophy , accounting , computer security , epistemology , quantum mechanics , business , programming language
Fisher information is of key importance in estimation theory. It is used as a tool for characterizing complex signals or systems, with applications, e.g. in biology, geophysics and signal processing. The problem of minimizing Fisher information in a set of distributions has been studied by many researchers. In this paper, based on some rather simple statistical reasoning, we provide an alternative proof for the fact that Gaussian distribution with finite variance minimizes the Fisher information over all distributions with the same variance.