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CATEGORIES:Information Theory Seminar
SUMMARY:Convexity properties of information functionals fo
 r Gaussian mixtures - Dr Lampros Gavalakis\, Gusta
 ve Eiffel University
DTSTART;TZID=Europe/London:20240117T140000
DTEND;TZID=Europe/London:20240117T150000
UID:TALK209251AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/209251
DESCRIPTION:We consider the entropy and Fisher information of 
 Gaussian mixtures\, that is centered Gaussians wit
 h randomly chosen variance. For the entropy\, we w
 ill show that a concavity conjecture of  Ball\, Na
 yar and Tkocz (2016) holds true for this class of 
 random variables.  For the Fisher information\, we
  will first present a simple upper bound. In order
  to extend this bound to higher dimensions\, we wi
 ll show that the Fisher information matrix is in g
 eneral operator convex as a matrix-valued function
 al of the density\, extending a result of Bobkov (
 2022). Finally\, as an application\, we will discu
 ss convergence rates for the Fisher information of
  weighted sums of Gaussian mixtures in the CLT.\n\
 nThis is joint work with Alexandros Eskenazis (Sor
 bonne and Cambridge).
LOCATION:MR5\, CMS Pavilion A
CONTACT:Dr Varun Jog
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