Always Good-Turing: Asymptotically optimal probability estimation

Alon Orlitsky, Narayana P. Santhanam, Junan Zhang

Research output: Contribution to journalConference article

Abstract

While deciphering the German Enigma code during World War II, I.J. Good and A.M. Turing considered the problem of estimating a probability distribution from a sample of data. They derived a surprising and unintuitive formula that has since been used in a variety of applications and studied by a number of researchers. Borrowing an information-theoretic and machine-learning framework, we define the attenuation of a probability estimator as the largest possible ratio between the per-symbol probability assigned to an arbitrarily-long sequence by any distribution, and the corresponding probability assigned by the estimator. We show that some common estimators have infinite attenuation and that the attenuation of the Good-Turing estimator is low, yet larger than one. We then derive an estimator whose attenuation is one, namely, as the length of any sequence increases, the per-symbol probability assigned by the estimator is at least the highest possible. Interestingly, some of the proofs use celebrated results by Hardy and Ramanujan on the number of partitions of an integer. To better understand the behavior of the estimator, we study the probability it assigns to several simple sequences. We show that for some sequences this probability agrees with our intuition, while for others it is rather unexpected.

Original languageEnglish (US)
Pages (from-to)179-188
Number of pages10
JournalAnnual Symposium on Foundations of Computer Science - Proceedings
StatePublished - Dec 2 2003
EventProceedings: 44th Annual IEEE Symposium on Foundations of Computer Science - FOCS 2003 - Cambridge, MA, United States
Duration: Oct 11 2003Oct 14 2003

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ASJC Scopus subject areas

  • Hardware and Architecture

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