X dP dμ ln dP dμ dμ on a measure space (X,M, μ), does not qualify itself as an information measure (it is not a natural extension of the discrete case), maximum entropy (ME) prescriptions in the measure-theoretic case are consistent with that of discrete case. In this paper, we study the measure-theoretic definitions of generalized information measures and discuss the ME prescriptions. We prese...

In this paper we compare two approaches to natural language understanding (NLU). The first approach is derived from the field of statistical machine translation (MT), whereas the other uses the maximum entropy (ME) framework. Starting with an annotated corpus, we describe the problem of NLU as a translation from a source sentence to a formal language target sentence. We mainly focus on the qual...

This paper presents a maximum entropy (ME)-based model for Chinese noun phrase metaphor recognition. The metaphor recognizing process will be viewed as a classification task between metaphor and literal meaning. Our experiments show that the metaphor recognizer based on the ME method is significantly better than the Example-based methods within the same context windows. In addition, performance...

This paper reviews and relates two default reasoning mechanisms, lexicographic (lex) and maximum entropy (me) entailment. Meentailment requires that defaults be assigned speci c strengths and it is shown that lex-entailment can be equated to me-entailment for a class of speci c strength assignments. By clarifying the assumptions which underlie lex-entailment, it is argued that me-entailment is ...

Organizations of many variables in nature such as soil moisture and topography exhibit patterns with no dominant scales. The maximum entropy (ME) principle is proposed to show how these variables can be statistically described using their scale-invariant properties and geometric mean. The ME principle predicts with great simplicity the probability distribution of a scale-invariant process in te...

Conditionals play a central part in knowledge representation and reasoning. Describing certain relationships between antecedents and consequences by \if{then{sentences" their range of expressiveness includes commonsense knowledge as well as scientiic statements. In this paper, we present the principles of maximum entropy resp. of minimum cross-entropy (ME-principles) as a logically sound and pr...

The method of Maximum (relative) Entropy (ME) is used to translate the information contained in the known form of the likelihood into a prior distribution for Bayesian inference. The argument is guided by intuition gained from the successful use of ME methods in statistical mechanics. For experiments that cannot be repeated the resulting “entropic prior” is formally identical with the Einstein ...

In this paper, at first we derive a family of maximum Tsallis entropy distributions under optional side conditions on the mean income and the Gini index. Furthermore, corresponding with these distributions a family of Lorenz curves compatible with the optional side conditions is generated. Meanwhile, we show that our results reduce to Shannon entropy as $beta$ tends to one. Finally, by using ac...

This paper introduces Hermite’s polynomials, in the description of quantum games. Hermite’s polynomials are associated with gaussian probability density. The gaussian probability density represents minimum dispersion. I introduce the concept of minimum entropy as a paradigm of both Nash’s equilibrium (maximum utility MU) and Hayek equilibrium (minimum entropy ME). The ME concept is related to Q...

In this paper we propose an approach to the estimation and simulation of loss distributions based on Maximum Entropy (ME), a non-parametric technique that maximizes the Shannon entropy of the data under moment constraints. Special cases of the ME density correspond to standard distributions; therefore, this methodology is very general as it nests most classical parametric approaches. Sampling t...