A Class of Distortionless Codes Designed by Bayes Decision Theory

研究成果: Article

29 引用 (Scopus)

抜粋

The problem of distortionless encoding when the parameters of the probabilistic model of a source are unknown is considered from a statistical decision theory point of view. A class of predictive and nonpredictive codes is proposed that are optimal within this framework. Specifically, it is shown that the codeword length of the proposed predictive code coincides with that of the proposed nonpredictive code for any source sequence. A bound for the redundancy for universal coding is given in terms of the supremum of the Bayes risk. If this supremum exists, then there exists a minimax code whose mean code length approaches it in the proposed class of codes, and the minimax code is given by the Bayes solution relative to the prior distribution of the source parameters that maximizes the Bayes risk.

元の言語English
ページ(範囲)1288-1293
ページ数6
ジャーナルIEEE Transactions on Information Theory
37
発行部数5
DOI
出版物ステータスPublished - 1991 9

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

フィンガープリント A Class of Distortionless Codes Designed by Bayes Decision Theory' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用