This monograph presents an overview of universal estimation of information measures for continuous-alphabet sources. Special attention is given to the estimation of mutual information and divergence based on independent and identically distributed (i.i.d.) data. Plug-in methods, partitioning-based algorithms, nearest-neighbor algorithms as well as other approaches are reviewed, with particular focus on consistency, speed of convergence and experimental performance.
|Original language||English (US)|
|Number of pages||89|
|Journal||Foundations and Trends in Communications and Information Theory|
|State||Published - 2008|
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Applied Mathematics