Year: 2011
Type: preprint
Abstract: In this paper we establish lower bounds on information divergence from a distribution to certain important classes of distributions as Gaussian, exponential, Gamma, Poisson, geometric, and binomial. These lower bounds are tight and for several convergence theorems where a rate of convergence can be computed, this rate is determined by the lower bounds proved in this paper. General techniques for getting lower bounds in terms of moments are developed.
Source: arXiv (Cornell University)
Authors: Harremoës, Peter, Vignat, Christophe
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1674462256