Prediction and Sequential Decision Making

M. Feder, N. Merhav, and M. Gutman,
``Universal prediction of individual sequences,''
IEEE Trans. Inform. Theory,
vol. 38, no. 4, pp. 12581270, July 1992 (received the 1993 paper award
of the Information Theory Society).

N. Merhav, M. Feder, and M. Gutman,
``Some properties
of sequential predictors
for binary Markov sources,''
IEEE Trans. Inform. Theory,
vol. 39, no. 3, pp. 887892, May 1993.

N. Merhav and M. Feder,
``Universal schemes for sequential decision from individual data
sequences,''
IEEE Trans. Inform. Theory
vol. 39, no. 4, pp. 12801291, July 1993.

R. Meir and N. Merhav,
``On the stochastic complexity
of learning realizable and unrealizable rules,''
Machine Learning vol. 19, no. 3, pp. 241261, 1995.

N. Merhav and M. Feder,
``Universal prediction,'' (invited paper)
IEEE Trans. Inform.
Theory, vol. 44, no. 6, pp. 21242147, October 1998.
(Commemorative issue for fifty years of Information Theory.)
Also, in Information Theory: 50 Years of Discovery,
pp. 80103, Eds.
S. Verdu and S. McLaughlin, IEEE Press, 1999.

A. Baruch and N. Merhav,
``Universal filtering and prediction of
individual sequences corrupted by noise
using the LempelZiv algorithm,''
Proc. 2000 IEEE Int. Symp. on Information Theory (ISIT 2000), p. 99, Sorrento, Italy, June 2000.

T. Weissman and N. Merhav,
``Universal prediction of individual binary
sequences in the presence of arbitrarily varying, memoryless, additive
noise,'' Proc. 2000 IEEE Int. Symp. on Information Theory (ISIT
2000), p. 97, Sorrento, Italy, June 2000.

T. Weissman, N. Merhav, and A. SomekhBaruch,
``Twofold universal prediction
schemes for achieving the finite state predictability of a noisy
individual binary sequence,''
IEEE Trans. Inform. Theory,
vol 47, no. 5, pp. 18491866, July 2001.

T. Weissman and N. Merhav,
``Universal prediction of binary individual
sequences in the presence of noise,''
IEEE Trans. Inform. Theory, vol. 47, no. 6, pp. 21512173,
September 2001.

N. Merhav, E. Ordentlich, G. Seroussi, and M. J. Weinberger,
``On sequential strategies for loss functions with memory,''
IEEE Trans. Inform. Theory, vol. 48, no. 7, pp. 19471958, July 2002.

N. Merhav and T. Weissman,
``Scanning and prediction in multidimensional data arrays,''
IEEE Trans. Inform. Theory, vol. 49, no. 1, pp. 6582, January 2003.

T. Weissman and N. Merhav,
``On competitive predictability and its relation to ratedistortion
theory and to channel capacity theory,''
IEEE Trans. Inform.
Theory, vol. 49, no. 12, pp. 31853194, December 2003.

T. Weissman and N. Merhav,
``Universal prediction of random binary
sequences in a noisy environment,''
Annals of Applied Probability,
vol. 14, no. 1, pp. 5489. February 2004.

E. Ordentlich, T. Weissman, M. J. Weinberger, A. SomekhBaruch, and
N. Merhav,
``Discrete universal filtering through incremental parsing,''
Proc. DCC 2004, Snowbird, Utah, March 2004.

E. Sabbag and N. Merhav,
``Large deviations performance of predictors
for Markov sources,'' Proc. ISIT 2004 , p. 11, Chicago, IL,
JuneJuly 2004.

J. Ziv and N. Merhav,
``On contexttree prediction of individual sequences,''
IEEE Trans. Inform. Theory, vol. 53, no. 5, pp. 18601866, May 2007.

T. Weissman, E. Ordentlich, M. J. Weinberger, A. SomekhBaruch, and N. Merhav,''
``Universal filtering via prediction,''
IEEE Trans. Inform. Theory, vol. 53, no. 4, pp. 12531264, April 2007.

A. Cohen, N. Merhav, and T. Weissman,
``Scanning and sequential decision making for
multidimensional data: part I  the noiseless case,''
IEEE Trans. Inform. Theory, vol. 53, no. 9, pp. 30013020,
September 2007.

A. Cohen, T. Weissman, and N. Merhav,
``Scanning and sequential decision making for
multidimensional data: part II  the noisy case,''
IEEE Trans. Inform. Theory, vol. 54, no. 12, pp. 56095631, December 2008.