Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions

By Xiang Gao, Meera Sitharam, Adrian E. Roitberg. 2017

75 citations recorded by the source corpus.

This paper gives upper and lower bounds on the gap in Jensen's inequality, i.e., the difference between the expected value of a function of a random variable and the value of the function at the expected value of the random variable. The bounds depend only on growth properties of the function and specific moments of the random variable. The bounds are particularly useful for distributions that are concentrated around the mean, a commonly occurring scenario.

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d60d25b3e9da5dcf31a3dd44dd1aeb9c13842f22

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Hyungjin Chung
2. Jong Chul Ye

In Statistics and Probability

1. Jeongsol Kim
2. Hyungjin Chung
3. Michael T. McCann
4. Jong Chul Ye
5. Marc Louis Klasky

Citations in the corpus

• Diffusion Posterior Sampling for General Noisy Inverse Problems (2022, background): “Proposition 2 (Jensen gap upper bound (Gao et al., 2017)) .”