A new variance formula is developed using the generalized inverse of an increasing function. Based on formula, a entropy for any uncertain variable provided. Most formulas in literature are special cases formula. Using maximum distribution unimodel variables provided without Euler–Lagrange equation.

Since 1990s, the maximum entropy model has been used in text categorization and achieves good results in Natural Language Processing since its framework and algorithm were established. On the basis of the Maximum Entropy Model, scholars improve it and make a more in-depth study. Using Maximum Entropy Model for text sentiment categorization has become a hot research topic in recent years. In thi...

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A new algorithm for computing the maximum entropy ranking over models is presented. The algorithm handles arbitrary sets of proposi-tional defaults with associated strength assignments and succeeds whenever the set satisses a robustness condition. Failure of this condition implies the problem may not be suuciently speciied for a unique solution to exist. This work extends the applicability of t...

We apply the principle of maximum entropy to select a unique joint probability distribution from the set of all joint probability distributions specified by a credal network. In detail, we start by showing that the unique joint distribution of a Bayesian tree coincides with the maximum entropy model of its conditional distributions. This result, however, does not hold anymore for general Bayesi...

The study of matrices with a displacement structure is mainly concerned with recursions for the so-called generator matrices. The recursion usually involves free parameters, which can be chosen in several ways so as to simplify the resulting algorithm. In this correspondence we present a choice for the parameters that is motivated by a maximumentropy formulation. This choice further motivates t...

This paper presents a new way to compute the probability distribution with maximum entropy satisfying a set of constraints. Unlike previous approaches , our method is integrated with the planning of data collection and tabulation. We show how adding constraints and performing the associated additional tabulations can substantially speed up computation by replacing the usual iterative techniques...

Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent) variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (suf...

This paper presents a Bayesian interpretation of maximum entropy image reconstruction and shows that exp(αS(/, m)), where S(f,m) is the entropy of image / relative to model m, is the only consistent prior probability distribution for positive, additive images. It also leads to a natural choice for the regularizing parameter α, that supersedes the traditional practice of setting χ = N. The new c...