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conditional differential random-variable entropy

См. также в других словарях:

  • Differential entropy — (also referred to as continuous entropy) is a concept in information theory that extends the idea of (Shannon) entropy, a measure of average surprisal of a random variable, to continuous probability distributions. Contents 1 Definition 2… …   Wikipedia

  • Entropy (information theory) — In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information… …   Wikipedia

  • Maximum entropy probability distribution — In statistics and information theory, a maximum entropy probability distribution is a probability distribution whose entropy is at least as great as that of all other members of a specified class of distributions. According to the principle of… …   Wikipedia

  • условная дифференциальная энтропия случайной величины — Дифференциальная энтропия условного распределения вероятностей случайной величины. [Сборник рекомендуемых терминов. Выпуск 94. Теория передачи информации. Академия наук СССР. Комитет технической терминологии. 1979 г.] Тематики теория передачи… …   Справочник технического переводчика

  • Information theory — Not to be confused with Information science. Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental… …   Wikipedia

  • probability theory — Math., Statistics. the theory of analyzing and making statements concerning the probability of the occurrence of uncertain events. Cf. probability (def. 4). [1830 40] * * * Branch of mathematics that deals with analysis of random events.… …   Universalium

  • Quantities of information — A simple information diagram illustrating the relationships among some of Shannon s basic quantities of information. The mathematical theory of information is based on probability theory and statistics, and measures information with several… …   Wikipedia

  • Kullback–Leibler divergence — In probability theory and information theory, the Kullback–Leibler divergence[1][2][3] (also information divergence, information gain, relative entropy, or KLIC) is a non symmetric measure of the difference between two probability distributions P …   Wikipedia

  • Cauchy distribution — Not to be confused with Lorenz curve. Cauchy–Lorentz Probability density function The purple curve is the standard Cauchy distribution Cumulative distribution function …   Wikipedia

  • Information theory and measure theory — Measures in information theory = Many of the formulas in information theory have separate versions for continuous and discrete cases, i.e. integrals for the continuous case and sums for the discrete case. These versions can often be generalized… …   Wikipedia

  • Multivariate normal distribution — MVN redirects here. For the airport with that IATA code, see Mount Vernon Airport. Probability density function Many samples from a multivariate (bivariate) Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the… …   Wikipedia

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