AbstractConsidered here are absolutely continuous probability distributions, concentrated on the interval [0, 1], and with the first M algebraic moments assigned. Lower and upper bounds for entropy are provided solely in terms of assigned moments
Shannon entropy of a probability distribution gives a weighted mean of a measure of information that...
Abstract — The differential entropy is a quantity employed ubiquitously in communications, statistic...
The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This ...
AbstractConsidered here are absolutely continuous probability distributions, concentrated on the int...
In many practical situations, we have only partial information about the probabilities. In some case...
The recovering of a positive density function of which a finite number of moments are assigned is co...
This paper extends maximum entropy estimation of discrete probability distributions to the continuou...
AbstractThe classical Stieltjes and Hamburger moment problems in the maximum entropy approach have b...
Continuous probability density functions and discrete probability mass functions are tabulated which...
Abstract—In many practical situations, we have only partial information about the probabilities. In ...
Given a finite number of moments of an unknown density ̅ x on a finite measure space, the best entro...
We study a parametric estimation problem related to moment condition models. As an alternative to th...
Traditionally, the Method of (Shannon-Kullback's) Relative Entropy Maximization (REM) is considered ...
International audienceWe consider the maximum entropy problems associated with Rényi $Q$-entropy, su...
Abstract — The differential entropy is a quantity employed ubiquitously in communications, statistic...
Shannon entropy of a probability distribution gives a weighted mean of a measure of information that...
Abstract — The differential entropy is a quantity employed ubiquitously in communications, statistic...
The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This ...
AbstractConsidered here are absolutely continuous probability distributions, concentrated on the int...
In many practical situations, we have only partial information about the probabilities. In some case...
The recovering of a positive density function of which a finite number of moments are assigned is co...
This paper extends maximum entropy estimation of discrete probability distributions to the continuou...
AbstractThe classical Stieltjes and Hamburger moment problems in the maximum entropy approach have b...
Continuous probability density functions and discrete probability mass functions are tabulated which...
Abstract—In many practical situations, we have only partial information about the probabilities. In ...
Given a finite number of moments of an unknown density ̅ x on a finite measure space, the best entro...
We study a parametric estimation problem related to moment condition models. As an alternative to th...
Traditionally, the Method of (Shannon-Kullback's) Relative Entropy Maximization (REM) is considered ...
International audienceWe consider the maximum entropy problems associated with Rényi $Q$-entropy, su...
Abstract — The differential entropy is a quantity employed ubiquitously in communications, statistic...
Shannon entropy of a probability distribution gives a weighted mean of a measure of information that...
Abstract — The differential entropy is a quantity employed ubiquitously in communications, statistic...
The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This ...
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