About: Krichevsky–Trofimov estimator

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In information theory, given an unknown stationary source π with alphabet A and a sample w from π, the Krichevsky–Trofimov (KT) estimator produces an estimate pi(w) of the probability of each symbol i ∈ A. This estimator is optimal in the sense that it minimizes the worst-case regret asymptotically. For a binary alphabet and a string w with m zeroes and n ones, the KT estimator pi(w) is defined as:

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  • In information theory, given an unknown stationary source π with alphabet A and a sample w from π, the Krichevsky–Trofimov (KT) estimator produces an estimate pi(w) of the probability of each symbol i ∈ A. This estimator is optimal in the sense that it minimizes the worst-case regret asymptotically. For a binary alphabet and a string w with m zeroes and n ones, the KT estimator pi(w) is defined as: (en)
  • K-T 추정량(Krichevsky–Trofimov estimator)이란 정보이론에서, A의 알파벳을 갖는 stationary source pi가 주어졌을 때, symbol 를 뽑을 확률 를 추정하는 추정량이다. K-T 추정량은 worst-case regret을 최소화한다. 일 때에 m개의 0과 n개의 1이 주어졌다면 K-T 추정량 P(m, n)은 다음과 같이 재귀적으로 정의된다: (ko)
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  • In information theory, given an unknown stationary source π with alphabet A and a sample w from π, the Krichevsky–Trofimov (KT) estimator produces an estimate pi(w) of the probability of each symbol i ∈ A. This estimator is optimal in the sense that it minimizes the worst-case regret asymptotically. For a binary alphabet and a string w with m zeroes and n ones, the KT estimator pi(w) is defined as: (en)
  • K-T 추정량(Krichevsky–Trofimov estimator)이란 정보이론에서, A의 알파벳을 갖는 stationary source pi가 주어졌을 때, symbol 를 뽑을 확률 를 추정하는 추정량이다. K-T 추정량은 worst-case regret을 최소화한다. 일 때에 m개의 0과 n개의 1이 주어졌다면 K-T 추정량 P(m, n)은 다음과 같이 재귀적으로 정의된다: (ko)
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  • Krichevsky–Trofimov estimator (en)
  • K-T 추정량 (ko)
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