The Differential Entropy of Mixtures: New Bounds and Applications

Mixture distributions are extensively used as a modeling tool in diverse areas from machine learning to communications engineering to physics, and obtaining bounds on the entropy of mixture distributions is of fundamental importance in many of these applications. This article provides sharp bounds o...

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Veröffentlicht in:	IEEE transactions on information theory 2022-04, Vol.68 (4), p.2123-2146
Hauptverfasser:	Melbourne, James, Talukdar, Saurav, Bhaban, Shreyas, Madiman, Mokshay, Salapaka, Murti V.
Format:	Artikel
Sprache:	eng
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">f -divergence Communications engineering Concavity differential entropy Entropy Extraterrestrial measurements Lower bounds Machine learning Mixture distributions Mixture models Mixtures Mutual information Random variables Thermodynamics Upper bound Upper bounds
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