H. Robbins, "Asymptotically subminimax solutions of compound statistical decision problems, " Proc. second Berkeley Symp. Math. Stat. Prob., pp. 131--148, 1951. Home/Search Document Not in Database Summary Related Articles Check |
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Twofold Universal Prediction Schemes for Achieving the.. - Weissman, Merhav, Baruch (2000) (Correct)
.... 1 Introduction Research pertaining to the problem of sequential decision making with an arbitrary deterministic binary sequence corrupted by noise, dates back to the fifties and sixties, where the compound decision problem was thoroughly investigated by statisticians, cf. 7] 14] 20] [24], 25] 27] 28] 34] 38] and references therein. With the dawning of the information age, the last decade has witnessed a wave of intensive interest in the problem of sequential prediction of individual sequences by researchers of many disciplines, such as information theory, learning ....
H. Robbins, "Asymptotically Subminimax Solutions of Compound Statistical Decision Problems," Proc. Second Berkeley Symp. Math. Statis. Prob., 131-148. Univ. of California Press. 39
Universal Portfolios with Side Information - Cover, Ordentlich (1996) (33 citations) (Correct)
....however, that aside from this motivational link, the present problem and solution possess no distributional or random aspect. Another source of motivation for considering the state constant rebalanced portfolios is the sequential compound Bayes decision problem of Robbins, Hannan, and others [7, 8, 9]. This problem involves a sequence of repeated plays of a game against nature. The goal is to exhibit a sequential player strategy which approximates the performance of the best constant player strategy determined in hindsight for any sequence of moves by nature. Our problem fits into this ....
H. Robbins. Asymptotically subminimax solutions of compound statistical decision problems. In Proc. 2nd Berkeley Symp. Math. Statistics. Probab., pages 131--148, 1951.
On-Line Algorithms in Machine Learning - Blum (1996) (12 citations) (Correct)
.... is that one can remove all statistical assumptions about the data and still achieve extremely tight bounds (see Freund [18] This problem and many variations and extensions have been addressed in a number of different communities, under names such as the sequential compound decision problem [32] [4] universal prediction [16] universal coding [33] universal portfolios [13] and prediction of individual sequences ; the notion of the competitiveness is also called the min max regret of an algorithm. A web page uniting some of these communities and with a discussion of this ....
H. Robbins. Asymptotically subminimax solutions of compound statistical decision problems. In Proc. 2nd Berkeley Symp. Math. Statist. Prob., pages 131--148, 1951.
Universal Prediction - Merhav, Feder (1998) (17 citations) (Correct)
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H. Robbins, "Asymptotically subminimax solutions of compound statistical decision problems, " Proc. second Berkeley Symp. Math. Stat. Prob., pp. 131--148, 1951.
Universal Prediction of Individual Binary Sequences in the.. - Weissman, Merhav (1999) (1 citation) (Correct)
No context found.
H. Robbins, "Asymptotically Subminimax Solutions of Compound Statistical Decision Problems," Proc. Second Berkeley Symp. Math. Statis. Prob., 131-148. Univ. of California Press.
Universal Portfolios - Cover (1996) (22 citations) (Correct)
No context found.
Robbins, H. (1951): "Asymptotically Subminimax Solutions of Compound Statistical Decision Problems," Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, Berkeley: University of California Press.
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