A Recurrence Theorem for Dependent Processes with Applications to Data Compression

IEEE Transactions on Information Theory

Volumn 38, Issue 5, 1992, Pages 1561-1564

  Nobel, Andrew ^a   Wyner, Aaron D ^b  

^a STANFORD UNIVERSITY   (United States)

^b LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PROBABILITY - RANDOM PROCESSES;

DEPENDENT PROCESSES; DITHERING; ERGODIC THEOREM; MARKOV CHAINS; RANDOM SEQUENCES; RECURRENCE THEOREM;

INFORMATION THEORY;

EID: 0026917864     PISSN: 00189448     EISSN: 15579654     Source Type: Journal    
DOI: 10.1109/18.149506     Document Type: Article

References (5)

1. P. Billingsley, Ergodic Theory and Information. New York: John Wiley, 1965.
2. R. C. Bradley, “Basic properties of strong mixing conditions,” in Progress in Probability and Statistics, E. Eberlein and M. S. Taqqu, Eds. Boston: Birkhäuser, 1986, pp. 165-192.
3. W. Feller, An Introduction to Probability Theory and Its Applications, 2nd ed. New York: John Wiley, 1957.
4. A. Wyner and J. Ziv, “Some asymptotic properties of the entropy of a stationary ergodic data source with applications to data compression,” IEEE Trans. Inform. Theory, vol. 35, pp. 1250-1258, Nov. 1989.
5. “Fixed data base version of the Lempel-Ziv data compression algorithm,” to appear in IEEE Trans. Inform. Theory.