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Asymptotic Divergences and Strong Dichotomy.

Authors :
Huang, Xiang
Lutz, Jack H.
Mayordomo, Elvira
Stull, Donald M.
Source :
IEEE Transactions on Information Theory. Oct2021, Vol. 67 Issue 10, p6296-6305. 10p.
Publication Year :
2021

Abstract

The Schnorr-Stimm dichotomy theorem (Schnorr and Stimm, 1972) concerns finite-state gamblers that bet on infinite sequences of symbols taken from a finite alphabet $\Sigma $. The theorem asserts that, for any such sequence $S$ , the following two things are true. (1) If $S$ is not normal in the sense of Borel (meaning that every two strings of equal length appear with equal asymptotic frequency in $S$), then there is a finite-state gambler that wins money at an infinitely-often exponential rate betting on $S$. (2) If $S$ is normal, then any finite-state gambler loses money at an exponential rate betting on $S$. In this paper we use the Kullback-Leibler divergence to formulate the lower asymptotic divergence ${\mathrm {div}}(S||\alpha)$ of a probability measure $\alpha $ on $\Sigma $ from a sequence $S$ over $\Sigma $ and the upper asymptotic divergence ${\mathrm {Div}}(S||\alpha)$ of $\alpha $ from $S$ in such a way that a sequence $S$ is $\alpha $ -normal (meaning that every string $w$ has asymptotic frequency $\alpha (w)$ in $S$) if and only if ${\mathrm {Div}}(S||\alpha)=0$. We also use the Kullback-Leibler divergence to quantify the total risk ${\mathrm {Risk}}_{G}(w)$ that a finite-state gambler $G$ takes when betting along a prefix $w$ of $S$. Our main theorem is a strong dichotomy theorem that uses the above notions to quantify the exponential rates of winning and losing on the two sides of the Schnorr-Stimm dichotomy theorem (with the latter routinely extended from normality to $\alpha $ -normality). Modulo asymptotic caveats in the paper, our strong dichotomy theorem says that the following two things hold for prefixes $w$ of $S$. ($1~'$) The infinitely-often exponential rate of winning in 1 is $2^{{\mathrm {Div}}(S||\alpha)|w|}$. ($2~'$) The exponential rate of loss in 2 is $2^{- {\mathrm {Risk}}_{G}(w)}$. We also use (1 $'$) to show that $1- {\mathrm {Div}}(S||\alpha)/c$ , where $c= \log (1/ \min _{a\in \Sigma }\alpha (a))$ , is an upper bound on the finite-state $\alpha $ -dimension of $S$ and prove the dual fact that $1- {\mathrm {div}}(S||\alpha)/c$ is an upper bound on the finite-state strong $\alpha $ -dimension of $S$. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189448
Volume :
67
Issue :
10
Database :
Academic Search Index
Journal :
IEEE Transactions on Information Theory
Publication Type :
Academic Journal
Accession number :
153710469
Full Text :
https://doi.org/10.1109/TIT.2021.3085425