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The variance and the entropy power of a continuous random variable are bounded from below by the reciprocal of its Fisher information through the Cram{e}r-Rao bound and the Stams inequality respectively. In this note, we introduce the Fisher information for discrete random variables and derive the discrete Cram{e}r-Rao-type bound and the discrete Stams inequality.
We extend Fanos inequality, which controls the average probability of events in terms of the average of some $f$--divergences, to work with arbitrary events (not necessarily forming a partition) and even with arbitrary $[0,1]$--valued random variable
In this paper, we analyze the impact of compressed sensing with complex random matrices on Fisher information and the Cram{e}r-Rao Bound (CRB) for estimating unknown parameters in the mean value function of a complex multivariate normal distribution.
We examine the role of information geometry in the context of classical Cramer-Rao (CR) type inequalities. In particular, we focus on Eguchis theory of obtaining dualistic geometric structures from a divergence function and then applying Amari-Nagoak
It is challenged only recently that the precision attainable in any measurement of a physical parameter is fundamentally limited by the quantum Cram{e}r-Rao Bound (QCRB). Here, targeting at measuring parameters in strongly dissipative systems, we pro
Single molecule localization microscopy has the potential to resolve structural details of biological samples at the nanometer length scale. However, to fully exploit the resolution it is crucial to account for the anisotropic emission characteristic