Optimal binary prefix-condition codes with ordinal probabilities

Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to determine an optimal binary prefix‐condition code. The estimates maximize the entropy subject to constraints specifying second‐level orderings of the letters' likelihoods. These are com...

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Veröffentlicht in:Journal of the American Society for Information Science Jg. 41; H. 5; S. 330 - 333
1. Verfasser: Greenberg, Irwin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington, D.C Wiley Subscription Services, Inc., A Wiley Company 01.07.1990
John Wiley & Sons
American Documentation Institute
Wiley Periodicals Inc
Schlagworte:
Algorithms
Alphabet letters
Applied sciences
Binary codes
Binary coding
Codes
Coding
Decision analysis
Decision theory
English language
Entropy
Estimates
Exact sciences and technology
Expected values
Information storage and retrieval
Information theory
Information work
Information, signal and communications theory
Letters (Correspondence)
Linear programming
Maximum entropy method
Operations research
Prefixes
Probability
Rule ordering
Subject indexing
Technical services
Telecommunications and information theory
Tennis
Alphabet
Letter
English
Huffman code
Estimation
Probability
Ordering
Method of maximum entropy
Algorithm
Information theory
ISSN:0002-8231, 1097-4571
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to determine an optimal binary prefix‐condition code. The estimates maximize the entropy subject to constraints specifying second‐level orderings of the letters' likelihoods. These are compared to estimates obtained from another probability estimation technique described in the decision theory literature using the English alphabet as an example. © 1990 John Wiley & Sons, Inc.
Bibliographie:ark:/67375/WNG-272RMD4C-3
istex:2DCB9C1272B18D974AE0959C13290BCF509E104E
ArticleID:ASI3
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Statistics/Data Report-1
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ObjectType-Article-1
ObjectType-Feature-2
ISSN:0002-8231
1097-4571
DOI:10.1002/(SICI)1097-4571(199007)41:5<330::AID-ASI3>3.0.CO;2-H