Gaussian assumption: The least favorable but the most useful

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Gaussian assumption is the most well-known and widely used distribution in many fields such as engineering, statistics, and physics. One of the major reasons why the Gaussian distribution has become so prominent is because of the central limit theorem (CLT) and the fact that the distribution of noise in numerous engineering systems is well captured by the Gaussian distribution. Moreover, features such as analytical tractability and easy generation of other distributions from the Gaussian distribution contributed further to the popularity of Gaussian distribution. Especially, when there is no information about the distribution of observations, Gaussian assumption appears as the most conservative choice. This follows from the fact that the Gaussian distribution minimizes the Fisher information, which is the inverse of the Cram?r-Rao lower bound (CRLB) (or equivalently stated, the Gaussian distribution maximizes the CRLB). Therefore, any optimization based on the CRLB under the Gaussian assumption can be considered to be min-max optimal in the sense of minimizing the largest CRLB (see [1] and the references cited therein).

Original languageEnglish
Article number6494684
Pages (from-to)183-186
Number of pages4
JournalIEEE Signal Processing Magazine
Volume30
Issue number3
DOIs
Publication statusPublished - 1 Jan 2013

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Gaussian distribution
Fisher Information
Tractability
Systems Engineering
Min-max
Systems engineering
Central limit theorem
Physics
Maximise
Statistics
Lower bound
Engineering
Minimise
Optimization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Gaussian assumption : The least favorable but the most useful. / Park, Sangwoo; Serpedin, Erchin; Qaraqe, Khalid.

In: IEEE Signal Processing Magazine, Vol. 30, No. 3, 6494684, 01.01.2013, p. 183-186.

Research output: Contribution to journalArticle

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