Using Onicescu's Informational Energy to Approximate Social Entropy
نویسندگان
چکیده
منابع مشابه
Motion Detection using Approximate Entropy
The detection of motion from image sequences is usually performed using a time differential method or calculating the difference from a background reference image. These algorithms often perform poorly in the presence of temporal clutter. This paper describes a technique that measures the irregularity of an intensity sequence to detect motion. We demonstrate that static background, interesting ...
متن کاملInformational entropy of Fourier maps.
An analysis on what is known as the interpretation of Fourier maps has been done from the information theory point of view: determining the nature of the peaks in the map (in order to assign them a suitable scattering factor) and allocating bonds between some of the possible peak pairs. Before interpreting the map, a quantitatively measurable entropy (uncertainty, unknowingness) relating to the...
متن کاملPhysiological time-series analysis using approximate entropy and sample entropy.
Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely relate...
متن کاملApproximate inference using conditional entropy decompositions
We introduce a novel method for estimating the partition function and marginals of distributions defined using graphical models. The method uses the entropy chain rule to obtain an upper bound on the entropy of a distribution given marginal distributions of variable subsets. The structure of the bound is determined by a permutation, or elimination order, of the model variables. Optimizing this ...
متن کاملApproximate entropy normalized measures for analyzing social neurobiological systems.
When considering time series data of variables describing agent interactions in social neurobiological systems, measures of regularity can provide a global understanding of such system behaviors. Approximate entropy (ApEn) was introduced as a nonlinear measure to assess the complexity of a system behavior by quantifying the regularity of the generated time series. However, ApEn is not reliable ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia - Social and Behavioral Sciences
سال: 2014
ISSN: 1877-0428
DOI: 10.1016/j.sbspro.2013.12.715