Fisher information of sampled packets: an application to flow size estimation
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Towards optimal sampling for flow size estimation
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Universal distributed estimation over multiple access channels with constant modulus signaling
IEEE Transactions on Signal Processing
| Hi-index | 754.84 |
The Fisher information J(X) of a random variable X under a translation parameter appears in information theory in the classical proof of the entropy-power inequality (EPI). It enters the proof of the EPI via the De-Bruijn identity, where it measures the variation of the differential entropy under a Gaussian perturbation, and via the convolution inequality J(X+Y)-1⩾J(X)-1+J(Y) -1 (for independent X and Y), known as the Fisher information inequality (FII). The FII is proved in the literature directly, in a rather involved way. We give an alternative derivation of the FII, as a simple consequence of a “data processing inequality” for the Cramer-Rao lower bound on parameter estimation