Abstract
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) |
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Pages (from-to) | 265-353 |
Number of pages | 89 |
Journal | Foundations and Trends in Communications and Information Theory |
Volume | 5 |
Issue number | 3 |
DOIs | |
State | Published - 2008 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Applied Mathematics