We consider augmenting abstract argumentation frameworks with probabilistic information and discuss different constraints to obtain meaningful probabilistic information. Moreover, we investigate the problem of incomplete probability assignments and propose a solution for completing these assignments by applying the principle of maximum entropy.

A brief discussion is given of the traditional version of the Maximum Entropy Method, including a review of some of the criticism that has been made in regard to its use in statistical inference. Motivated by these questions, a modified version of the method is then proposed and applied to an example in order to demonstrate its use with a given time series.

We consider a differential method of maximum entropy that is based on the linearity of Fourier transform and involves reconstruction of images from the differences of the visibility function. The efficiency of the method is demonstrated with respect to the recovery of source images with bright components against the background of a sufficiently weak extended base. The simulation results are giv...

In this paper, we investigate the usefulness of a wide range of features for their usefulness in the resolution of nominal coreference, both as hard constraints (i.e. completely removing elements from the list of possible candidates) as well as soft constraints (where a cumulation of violations of soft constraints will make it less likely that a candidate is chosen as the antecedent). We presen...

This paper introduces a maximum entropy method to Discourse Coherence Modeling (DCM). Different from the state-of-art supervised entity-grid model and unsupervised cohesion-driven model, the model we proposed only takes as input lexicon features, which increases the training speed and decoding speed significantly. We conduct an evaluation on two publicly available benchmark data sets via senten...

The application of the Maximum Entropy method for sky imaging with the SPI instrument on INTEGRAL is described. While intended primarily for extended emission, point sources are also mapped by this method. The technique is implemented as the program spiskymax in the data analysis distribution provided by the INTEGRAL Science Data Centre. We briefly introduce the method, describe the particular ...

In this paper we consider the problem of estimating a parameter of a probability distribution when we have some prior information on a nuisance parameter. We start by the very simple case where we know perfectly the value of the nuisance parameter. The complete likelihood is the classical tool in this case. Then, progressively, we consider the case where we are given a prior probability distrib...

Weak gravitational lensing studies of clusters of galaxies provide complimentary information to that from X-ray data; the measured signal depends only on the cluster’s projected mass, independent of dynamical assumptions. Here we apply a maximum–entropy method algorithm for reconstructing the two-dimensional density distribution to two sets of shear data for the high redshift cluster MS1054-03,...

In probabilistic logic programming, given a query, either a probability interval or a precise probability obtained by using the maximum entropy principle is returned for the query. The former can be noninformative (e.g., interval [0, 1]) and the reliability of the latter is questionable when the priori knowledge is imprecise. To address this problem, in this paper, we propose some methods to qu...

A long standing mystery in using Maximum Entropy (MaxEnt) is how to deal with constraints whose values are uncertain. This situation arises when constraint values are estimated from data, because of finite sample sizes. One approach to this probiem, advocated by E.T. Jaynes [1], is to igriore this uncertainty, and treat the empirically observed values as exact. We refer to this as the classic M...