A Class of Distortionless Codes Designed by Bayes Decision Theory

研究成果: Article査読

46 被引用数 (Scopus)


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.

ジャーナルIEEE Transactions on Information Theory
出版ステータスPublished - 1991 9月

ASJC Scopus subject areas

  • 情報システム
  • コンピュータ サイエンスの応用
  • 図書館情報学


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