On Relations Between the Relative Entropy and
Markov chains
chi-squared divergence
f-divergences
information contraction
large deviations
maximal correlation
method of types
relative entropy
strong data–processing inequalities
Journal
Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874
Informations de publication
Date de publication:
18 May 2020
18 May 2020
Historique:
received:
22
04
2020
revised:
12
05
2020
accepted:
17
05
2020
entrez:
8
12
2020
pubmed:
9
12
2020
medline:
9
12
2020
Statut:
epublish
Résumé
The relative entropy and the chi-squared divergence are fundamental divergence measures in information theory and statistics. This paper is focused on a study of integral relations between the two divergences, the implications of these relations, their information-theoretic applications, and some generalizations pertaining to the rich class of
Identifiants
pubmed: 33286335
pii: e22050563
doi: 10.3390/e22050563
pmc: PMC7848888
pii:
doi:
Types de publication
Journal Article
Langues
eng
Références
Entropy (Basel). 2018 May 19;20(5):
pubmed: 33265473
Entropy (Basel). 2019 Dec 30;22(1):
pubmed: 33285826
Entropy (Basel). 2020 Feb 16;22(2):
pubmed: 33285995