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pro vyhledávání: '"K V, Harsha"'
Autor:
K V, Harsha, Subramanyam, Alladi
In this paper, we first describe the generalized notion of Cramer-Rao lower bound obtained by Naudts (2004) using two families of probability density functions, the original model and an escort model. We reinterpret the results in Naudts (2004) from
Externí odkaz:
http://arxiv.org/abs/1802.04483
Autor:
K. V. Harsha, Alladi Subramanyam
Publikováno v:
Annals of the Institute of Statistical Mathematics. 72:1237-1256
In this paper, we first describe the generalized notion of Cramer–Rao lower bound obtained by Naudts (J Inequal Pure Appl Math 5(4), Article 102, 2004) using two families of probability density functions: the original model and an escort model. We
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 433:136-147
An exponential family is dually flat with respect to Amari’s ± 1 connection. A deformed exponential family which is a generalization of the exponential family has two dually flat structures called the U -geometry and the χ -geometry. In the case
Autor:
K. V., Harsha1 harsha.11@iist.ac.in, K. S., Subrahamanian Moosath1 smoosath@iist.ac.in
Publikováno v:
Entropy. May2014, Vol. 16 Issue 5, p2472-2487. 16p.
Publikováno v:
Turkish Journal of Physiotherapy Rehabilitation; 2021, Vol. 32 Issue 2, p1024-1031, 8p
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319250397
GSI
GSI
A deformed exponential family has two kinds of dual Hessian structures, the U-geometry and the \(\chi \)-geometry. In this paper, we discuss the relation between the non-invariant (F, G)-geometry and the two Hessian structures on a deformed exponenti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea1b234883ddfa149d888107d8accf85
https://doi.org/10.1007/978-3-319-25040-3_24
https://doi.org/10.1007/978-3-319-25040-3_24
Publikováno v:
AIP Conference Proceedings.
We consider a family of probability distributions called F-exponential family which has got a dually flat structure obtained by the conformal flattening of the (F,G)-geometry. Geometry of F-likelihood estimator is discussed and the F-version of the m