Practical Universal Noiseless Coding

Discrete data sources arising from practical problems are generally characterized by only partially known and varying statistics. This paper provides the development and analysis of some practical adaptive techniques for the efficient noiseless coding of a broad class of such data sources. Specifically, algorithms are developed for coding discrete memoryless sources which have a known symbol probability ordering but unknown probability values. A general applicability of these algorithms is obtained because most real world problems can be simply transformed into this form by appropriate preprocessing.