Dilation of Chisini-Jensen-Shannon Divergence

2016 ◽  
Author(s):  
Piyush Kumar Sharma ◽  
Gary Holness
Keyword(s):  
Jensen Shannon Divergence

scholarly journals On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 464
Author(s):  
Frank Nielsen
Keyword(s):  
Diversity Index ◽  
Diversity Indices ◽  
Shannon Diversity Index ◽  
Probability Measures ◽  
Variational Optimization ◽  
Shannon Diversity ◽  
Concept Of Information ◽  
Jensen Shannon Divergence

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

scholarly journals APPLICATIONS OF DENSITY MATRICES IN A TRAPPED BOSE GAS

2006 ◽  
Vol 20 (15) ◽  
pp. 2189-2221 ◽  
Author(s):  
K. CH. CHATZISAVVAS ◽  
S. E. MASSEN ◽  
CH. C. MOUSTAKIDIS ◽  
C. P. PANOS
Keyword(s):  
Quantum Information ◽  
Bose Gas ◽  
Einstein Condensation ◽  
Density Distributions ◽  
Occupation Numbers ◽  
The One ◽  
Bose Einstein ◽  
Jensen Shannon Divergence ◽  
Analytical Expressions ◽  
Short Range Correlations

An overview of the Bose–Einstein condensation of correlated atoms in a trap is presented by examining the effect of interparticle correlations to one- and two-body properties of the above systems at zero temperature in the framework of the lowest order cluster expansion. Analytical expressions for the one- and two-body properties of the Bose gas are derived using Jastrow-type correlation function. In addition numerical calculations of the natural orbitals and natural occupation numbers are also carried out. Special effort is devoted for the calculation of various quantum information properties including Shannon entropy, Onicescu informational energy, Kullback–Leibler relative entropy and the recently proposed Jensen–Shannon divergence entropy. The above quantities are calculated for the trapped Bose gases by comparing the correlated and uncorrelated cases as a function of the strength of the short-range correlations. The Gross–Piatevskii equation is solved, giving the density distributions in position and momentum space, which are employed to calculate quantum information properties of the Bose gas.

Ventricular Tachycardia and Ventricular Fibrillation Analysis Based on Jensen-Shannon Divergence Analysis

2013 ◽  
Vol 321-324 ◽  
pp. 712-715
Author(s):  
Zheng Zhong Zheng ◽  
Jun Chang Zhao ◽  
Jun Wang
Keyword(s):  
Ventricular Tachycardia ◽  
Ventricular Fibrillation ◽  
Sinus Rhythm ◽  
Complexity Analysis ◽  
Heart Function ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Practice Of Medicine ◽  
Cardiac Sudden Death ◽  
Jensen Shannon Divergence

t is an important method for using electrocardiogram (ECG) to detect and diagnose heart function in clinical practice of medicine. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are serious threats for peoples lives, they often bring about cardiac sudden death. In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of the normal sinus rhythm signals, VT, VF. The study found that the VF was highest, followed by that of VT, and that of normal sinus rhythm signals was minimum. The result can be used to assisted clinical diagnosis.

scholarly journals A Jensen-Shannon Divergence Kernel for Directed Graphs

2016 ◽  
pp. 196-206 ◽  
Author(s):  
Cheng Ye ◽  
Richard C. Wilson ◽  
Edwin R. Hancock
Keyword(s):  
Directed Graphs ◽  
Jensen Shannon Divergence

scholarly journals Learning-Based 2D/3D Rigid Registration Using Jensen-Shannon Divergence for Image-Guided Surgery

2006 ◽  
pp. 228-235 ◽  
Author(s):  
Rui Liao ◽  
Christoph Guetter ◽  
Chenyang Xu ◽  
Yiyong Sun ◽  
Ali Khamene ◽  
...  
Keyword(s):  
Image Guided Surgery ◽  
Rigid Registration ◽  
Guided Surgery ◽  
Image Guided ◽  
Jensen Shannon Divergence

scholarly journals Discrimination Measure of Correlations in a Population of Neurons by Using the Jensen-Shannon Divergence

2007 ◽  
Author(s):  
F. Montani ◽  
O. A. Rosso ◽  
S. R. Schultz
Keyword(s):  
Jensen Shannon Divergence
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