Title :
Minimum entropy of error estimation for discrete random variables
Author :
M. Janzura;T. Koski;A. Otahal
Author_Institution :
Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic
Abstract :
The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, we show that the MEEE is given by a Neyman-Pearson-type strictly monotonous test. In addition, the asymptotic behavior of the error probabilities is proved to be equivalent to that of the Bayesian test.
Keywords :
"Entropy","Error analysis","Random variables","Testing","Bayesian methods","State estimation","Estimation error","Error probability","Predictive coding"
Journal_Title :
IEEE Transactions on Information Theory
