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In the maximal entropy principle, assuming stationarity, one looks for the probability distribution which maximizes the statistical entropy given those constraints.
In statistical physics, they are usually derived from the maximal entropy principle [14].
The infinite range of the potential corresponds, in the maximal entropy principle interpretation, to having infinitely many constraints.
In this way, the truncated potential corresponds to a finite number of constraints in the maximal entropy principle interpretation.
The most known ones for opinion mining are: Naïve Bayesian classification, maximal entropy principle, and the support vector machine.
As it is well known from Statistical Mechanics, the exponential distribution (1) can be derived from the Maximal Entropy Principle under some minimal assumptions [20].
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Section "Maximum entropy analysis" presents maximum entropy principle to faciliate approximate results for waiting time of the retrial model.
Known maximum entropy principles appear as corollaries and new ones are derived.
In the 'Maximum entropy results' section, maximum entropy principle is discussed to find the queue size distribution.
The Gibbsian maximum entropy principle then requires that SG be maximal, given the constraints that are imposed on the system.
The principle of maximum entropy is described in the 'Maximum entropy principle' section to establish the approximate results for the expected system size and expected waiting time.
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