Zobrazeno 1 - 10
of 179
pro vyhledávání: '"Kumar M Ashok"'
Publikováno v:
E3S Web of Conferences, Vol 399, p 02008 (2023)
The world is beginning to move away from its consumption of fossil fuels. Various technologies are being developed to make use of renewable energy sources such as wind, solar, and tidal, etc. Solar energy is the best choice among these sources becaus
Externí odkaz:
https://doaj.org/article/d1bda03c206746909869172d4997fe4b
Information geometry is a study of statistical manifolds, that is, spaces of probability distributions from a geometric perspective. Its classical information-theoretic applications relate to statistical concepts such as Fisher information, sufficien
Externí odkaz:
http://arxiv.org/abs/2310.03884
Autor:
Gayen, Atin, Kumar, M. Ashok
Publikováno v:
IEEE Transactions on Information Theory 69 (12) 7565 - 7583 (2023)
This paper generalizes the notion of sufficiency for estimation problems beyond maximum likelihood. In particular, we consider estimation problems based on Jones et al. and Basu et al. likelihood functions that are popular among distance-based robust
Externí odkaz:
http://arxiv.org/abs/2205.00530
Autor:
S, Uma Maheswari, Alphy, Anna, Deivasikamani, Ganeshkumar, Prakash, U., Shadrach, Finney Daniel, Kumar, M. Ashok, Manoj, S.
Publikováno v:
In Groundwater for Sustainable Development May 2024 25
Autor:
Mishra, Kumar Vijay, Kumar, M. Ashok
We examine the role of information geometry in the context of classical Cram\'er-Rao (CR) type inequalities. In particular, we focus on Eguchi's theory of obtaining dualistic geometric structures from a divergence function and then applying Amari-Nag
Externí odkaz:
http://arxiv.org/abs/2104.01061
This paper establishes a close relationship among the four information theoretic problems, namely Campbell source coding, Arikan guessing, Huleihel et al. memoryless guessing and Bunte and Lapidoth tasks partitioning problems. We first show that the
Externí odkaz:
http://arxiv.org/abs/2012.13707
Generalized Bayesian Cram\'{e}r-Rao Inequality via Information Geometry of Relative $\alpha$-Entropy
Autor:
Mishra, Kumar Vijay, Kumar, M. Ashok
The relative $\alpha$-entropy is the R\'enyi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the Cram\'{e}r-Rao inequ
Externí odkaz:
http://arxiv.org/abs/2002.04732
Autor:
Kumar, M. Ashok, Mishra, Kumar Vijay
We study the geometry of probability distributions with respect to a generalized family of Csisz\'ar $f$-divergences. A member of this family is the relative $\alpha$-entropy which is also a R\'enyi analog of relative entropy in information theory an
Externí odkaz:
http://arxiv.org/abs/2001.04769
We study four problems namely, Campbell's source coding problem, Arikan's guessing problem, Huieihel et al.'s memoryless guessing problem, and Bunte and Lapidoth's task partitioning problem. We observe a close relationship among these problems. In al
Externí odkaz:
http://arxiv.org/abs/1907.06889
Autor:
Gayen, Atin, Kumar, M. Ashok
We extend projection theorems concerning Hellinger and Jones et al. divergences to the continuous case. These projection theorems reduce certain estimation problems on generalized exponential models to linear problems. We introduce the notion of regu
Externí odkaz:
http://arxiv.org/abs/1905.01434