English

Information Geometry and Classical Cram\'{e}r-Rao Type Inequalities

Information Theory 2021-08-24 v3 Machine Learning Signal Processing Differential Geometry math.IT Machine Learning

Abstract

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-Nagoaka's theory to obtain a CR type inequality. The classical deterministic CR inequality is derived from Kullback-Leibler (KL)-divergence. We show that this framework could be generalized to other CR type inequalities through four examples: α\alpha-version of CR inequality, generalized CR inequality, Bayesian CR inequality, and Bayesian α\alpha-CR inequality. These are obtained from, respectively, IαI_\alpha-divergence (or relative α\alpha-entropy), generalized Csisz\'ar divergence, Bayesian KL divergence, and Bayesian IαI_\alpha-divergence.

Cite

@article{arxiv.2104.01061,
  title  = {Information Geometry and Classical Cram\'{e}r-Rao Type Inequalities},
  author = {Kumar Vijay Mishra and M. Ashok Kumar},
  journal= {arXiv preprint arXiv:2104.01061},
  year   = {2021}
}

Comments

34 pages, 2 figures, 1 table, book chapter. arXiv admin note: text overlap with arXiv:2001.04769

R2 v1 2026-06-24T00:48:23.698Z