A method, which we suggest to call the Empirical Maximum Entropy method, is implicitly present at Maximum Entropy Empirical Likelihood method, as its special, non-parametric case. From this vantage point the entropy-based empirical approach to estimation is surveyed.

The game which can be taken to lie behind the maximum entropy principle is studied. Refining previous techniques, we present a comprehensive and satisfactory theoretical discussion of the fundamentals of this game in its simplest setting. The results are illustrated by concrete examples pertaining to well known classical models.

Based on the principle of maximum entropy the primary morphologic equation is derived, and then the equations for hydraulic geometry of longitudinal profile and crosssection are established. For V-shaped cross-sections the relevant morphologic equations which are derived are compared with the existing empirical and semi-empirical formulae. They show good agreement with the prototypes.

Analysis of speech acts and discourse structures is essential to a dialogue understanding system because speech acts and discourse structures are closely tied with the speaker’s intention. However, it has been difficult to infer a speech act and a discourse structure from a surface utterance because they highly depend on the context of the utterance. We propose a statistical dialogue analysis m...

We show that, to generate the statistical operator appropriate for a given system, and as an alternative to Jaynes’ MaxEnt approach, that refers to the entropy S, one can use instead the increments δS in S. To such an effect, one uses the macroscopic thermodynamic relation that links δS to changes in i) the internal energy E and ii) the remaining M relevant extensive quantities Ai, i = 1, . . ....

This study addresses the problem of automatically detecting decisions in conversational speech. We formulate the problem as classifying decision-making units at two levels of granularity: dialogue acts and topic segments. We conduct an empirical analysis to determine the characteristic features of decision-making dialogue acts, and train MaxEnt models using these features for the classification...

We represent knowledge by using probability distributions of mixed continuous and discrete variables. In the case of complete knowledge of the joint distribution of all items, one can compute arbitrary conditional distributions, which may be used for prediction. However, in many cases only some marginal distributions, inverse probabilities, or moments are known. Under these conditions, a princi...

of the PhD thesis

In this work, we make an exhaustive study of the properties of the Green’s function related to the periodic boundary value problem Lax ≡ x′′ + a(t) x = 0, x(0) = x(T ), x′(0) = x′(T ), with a sign-changing potential a(t). Moreover, we obtain new explicit criteria that ensures that the maximum or anti-maximum principle holds for this equation. The given criteria complement previous results in th...

For each transition probability, MEMM uses maximum entropy model. Maximum entropy model is used to model the data distribution, which gives us as much information as possible. Without any constraints, the uniform distribution gives us maximum entropy. However, we have training data which gives some facts about the true distribution. What maximum entropy model does is to maximize the entropy of ...