In this paper, we have obtained bounds on Csiszár’s f-divergence in terms of relative information of type s using Dragomir’s [9] approach. The results obtained in particular lead us to bounds in terms of χ−Divergence, Kullback-Leibler’s relative information and Hellinger’s discrimination.

We address the problem of identifying key domain concepts automatically from an unannotated corpus of goal-oriented human-human conversations. We examine two clustering algorithms, one based on mutual information and another one based on Kullback-Liebler distance. In order to compare the results from both techniques quantitatively, we evaluate the outcome clusters against reference concept labe...

BACKGROUND To select a proper diagnostic test, it is recommended that the most specific test be used to confirm (rule in) a diagnosis, and the most sensitive test be used to establish that a disease is unlikely (rule out). These rule-in and rule-out concepts can also be characterized by the likelihood ratio (LR). However, previous papers discussed only the case of binary tests and assumed test ...

We introduce a three-parameter generalized normal distribution, which belongs to the Kotz type distribution family, to study the generalized entropy type measures of information. For this generalized normal, the Kullback-Leibler information is evaluated, which extends the well known result for the normal distribution, and plays an important role for the introduced generalized information measur...

The extension of classical analysis to time series data is the basic problem faced in many fields, such as engineering, economic and medicine. The main objective of discriminant time series analysis is to examine how far it is possible to distinguish between various groups. There are two situations to be considered in the linear time series models. Firstly when the main discriminatory informati...