Informational Confidence Bounds for Self-Normalized Averages and Applications

Aurélien Garivier

doi:10.1109/itw.2013.6691311

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arXiv · 1309.3376

Informational Confidence Bounds for Self-Normalized Averages and Applications

Aurélien Garivier

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Abstract

We present deviation bounds for self-normalized averages and applications to estimation with a random number of observations. The results rely on a peeling argument in exponential martingale techniques that represents an alternative to the method of mixture. The motivating examples of bandit problems and context tree estimation are detailed.

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Aurélien Garivier. 2013-09-13. Informational Confidence Bounds for Self-Normalized Averages and Applications. https://doi.org/10.1109/itw.2013.6691311

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