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
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Abstract	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.
AbstractList	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. 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 maximise the entropy subject to constraints specifying Second-level orderings of the letters' likelihoods. These are compared to estimates obtained from the decision theory literature using the English alphabet as an example. 00 Original abstract 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. Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to de- termine an optimal binary prefix-condition code. The es- timates maximize the entropy subject to constraints specifying second-level orderings of the letters' likeli- hoods. These are compared to estimates obtained from another probability estimation technique described in the decision theory literature using the English alpha- bet as an example.
Author	Greenberg, Irwin
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Copyright	Copyright © 1990 John Wiley & Sons, Inc. 1990 INIST-CNRS Copyright Wiley Periodicals Inc. Jul 1990
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References	Thomas, M. U. (1979). A generalized maximum entropy principle. Operations Research, 27, 1185-1196. Montgomery, B. L., & Abrahams, J. (1987). On the redundancy of optimal binary prefix-condition codes for finite and infinite sources. IEEE Transactions on Information Theory, IT-33, 156-160. Shore, J. E., & Johnson, R. W. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory. IT-26, 26-37. Eisner, H. (1988). Computer-aided systems engineering. Englewood Cliffs: Prentice Hall. Fishburn, P. C. (1965). Analysis of decisions with incomplete knowledge of probabilities. Operations Research, 13, 217-222. Johnsen, D. (1980). On the redundancy of binary Huffman codes. IEEE Transactions on Information Theory, IT-26, 220-222. Freund, D., Saxena, U. (1984). An algorithm for a class of discrete maximum entropy problems. Operations Research, 32, 210-215. Gallager, R. G. (1975). Optimal source codes for geometrically distributed integer alphabets. IEEE Transactions on Information Theory, IT-21, 228-230. 1979; 27 1965; 13 1975; IT‐21 1980; IT‐26 1984; 32 1987; IT‐33 1988
References_xml	– reference: Freund, D., Saxena, U. (1984). An algorithm for a class of discrete maximum entropy problems. Operations Research, 32, 210-215. – reference: Gallager, R. G. (1975). Optimal source codes for geometrically distributed integer alphabets. IEEE Transactions on Information Theory, IT-21, 228-230. – reference: Johnsen, D. (1980). On the redundancy of binary Huffman codes. IEEE Transactions on Information Theory, IT-26, 220-222. – reference: Montgomery, B. L., & Abrahams, J. (1987). On the redundancy of optimal binary prefix-condition codes for finite and infinite sources. IEEE Transactions on Information Theory, IT-33, 156-160. – reference: Thomas, M. U. (1979). A generalized maximum entropy principle. Operations Research, 27, 1185-1196. – reference: Fishburn, P. C. (1965). Analysis of decisions with incomplete knowledge of probabilities. Operations Research, 13, 217-222. – reference: Eisner, H. (1988). Computer-aided systems engineering. Englewood Cliffs: Prentice Hall. – reference: Shore, J. E., & Johnson, R. W. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory. IT-26, 26-37. – volume: IT‐33 start-page: 156 year: 1987 end-page: 160 article-title: On the redundancy of optimal binary prefix‐condition codes for finite and infinite sources publication-title: IEEE Transactions on Information Theory – volume: IT‐21 start-page: 228 year: 1975 end-page: 230 article-title: Optimal source codes for geometrically distributed integer alphabets publication-title: IEEE Transactions on Information Theory – volume: IT‐26 start-page: 26 year: 1980 end-page: 37 article-title: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross‐entropy publication-title: IEEE Transactions on Information Theory – volume: 32 start-page: 210 year: 1984 end-page: 215 article-title: An algorithm for a class of discrete maximum entropy problems publication-title: Operations Research – volume: IT‐26 start-page: 220 year: 1980 end-page: 222 article-title: On the redundancy of binary Huffman codes publication-title: IEEE Transactions on Information Theory – volume: 27 start-page: 1185 year: 1979 end-page: 1196 article-title: A generalized maximum entropy principle publication-title: Operations Research – year: 1988 – volume: 13 start-page: 217 year: 1965 end-page: 222 article-title: Analysis of decisions with incomplete knowledge of probabilities publication-title: Operations Research
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Snippet	Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to determine an optimal binary prefix‐condition... Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to de- termine an optimal binary prefix-condition... Probability estimates for letters in a finite alphabet are derived in order to apply the Huffman algorithm to determine an optimal binary prefix-condition...
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SubjectTerms	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
Title	Optimal binary prefix-condition codes with ordinal probabilities
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