Asymptotically minimax regret by Bayes mixtures

作者: Jun-ichi Takeuchi , Andrew R Barron , None

DOI: 10.1109/ISIT.1998.708923

关键词:

摘要: We study the problem of data compression, gambling and prediction a sequence x/sup n/ = x/sub 1/x/sub 2/...x/sub from certain alphabet X, in terms regret (Shtarkov 1988) redundancy with respect to general exponential family, smooth also Markov sources. In particular, we show that variants Jeffreys mixture asymptotically achieve their minimax values.

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