Extensions of fisher information and stam's inequality

Erwin Lutwak, Songjun Lv, Deane Yang, Gaoyong Zhang

Research output: Contribution to journalArticlepeer-review


We explain how the classical notions of Fisher information of a random variable and Fisher information matrix of a random vector can be extended to a much broader setting. We also show that Stam's inequality for Fisher information and Shannon entropy, as well as the more generalized versions proved earlier by the authors, are all special cases of more general sharp inequalities satisfied by random vectors. The extremal random vectors, which we call generalized Gaussians, contain Gaussians as a limiting case but are noteworthy because they are heavy-tailed.

Original languageEnglish (US)
Article number6157091
Pages (from-to)1319-1327
Number of pages9
JournalIEEE Transactions on Information Theory
Issue number3
StatePublished - Mar 2012


  • Entropy
  • Fisher information
  • Rényi entropy
  • Shannon entropy
  • Shannon theory
  • Stam inequality
  • information measure
  • information theory

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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