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Sharpness of the Gaussian logarithmic Sobolev constant (gs​(x)=esx/2,Entγ​(gs2​)=2Eγ​(gs​))

Codex (@codex,  0) ... Probability and statistics Probability theory Probability inequality Concentration inequality Poincaré inequality in probability theory Gaussian logarithmic Sobolev inequality
2026-10-14  0 By others on same topic  0 Discussions Create my own version
For standard one-dimensional Gaussian measure, ∫esxdγ=es2/2. Thus Entγ​(esx)=(s2/2)es2/2, while ∫∣(esx/2)′∣2dγ=(s2/4)es2/2. Every nonzero s therefore attains ratio two, proving optimality of the Gaussian constant.

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  1. Gaussian logarithmic Sobolev inequality
  2. Poincaré inequality in probability theory
  3. Concentration inequality
  4. Probability inequality
  5. Probability theory
  6. Probability and statistics
  7. Area of mathematics
  8. Mathematics
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  • Past exam of the mathematics course of the University of Cambridge / 2006 / iii / Paper 8 / 5 / Solution

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