<p>By measures of the difference between median values , the Kolmogorov-Smirnov distance , and the Kullback-Leibler divergence , the cumulative distributions for <i>INT</i>, <i>NON</i>, and <i>EXP</i> are nearly identical. Since <i>φ</i> is an angle measured in radians, the values of should be compared to the value 2<i>π</i>. The three distributions are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083343#pone-0083343-g006" target="_blank">Fig. 6(B)</a>.</p
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...
Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics....
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...
<p>By measures of the difference between median values (in <i>m</i>), the Kolmogorov-Smirnov distan...
Journal PaperWe define a new distance measure - the resistor-average distance - between two probabil...
<p>Euclidean distance between the motion mode probability distribution functions is shown, intra-dya...
<p>The distributions contain the pooled data from all nine subjects for the angles obtained from the...
(a)-(b) are generated using the fidelity, F, and (c)-(d) using the Kolmogorov distance, K, for neare...
<p>From left to right Euclidean distance between samples collected from the same individuals 0 month...
We consider a situation where two sample sets of independent real valued observations are obtained f...
<p>(a) The fraction of players that have chosen to cooperate is decreasing over time, but remains su...
Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distr...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
Van Lieshout and Baddeley introduced the function J = (1 \Gamma G)=(1 \Gamma F ) as a measure of int...
One natural way to measure model adequacy is by using statistical distances as loss functions. A rel...
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...
Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics....
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...
<p>By measures of the difference between median values (in <i>m</i>), the Kolmogorov-Smirnov distan...
Journal PaperWe define a new distance measure - the resistor-average distance - between two probabil...
<p>Euclidean distance between the motion mode probability distribution functions is shown, intra-dya...
<p>The distributions contain the pooled data from all nine subjects for the angles obtained from the...
(a)-(b) are generated using the fidelity, F, and (c)-(d) using the Kolmogorov distance, K, for neare...
<p>From left to right Euclidean distance between samples collected from the same individuals 0 month...
We consider a situation where two sample sets of independent real valued observations are obtained f...
<p>(a) The fraction of players that have chosen to cooperate is decreasing over time, but remains su...
Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distr...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
Van Lieshout and Baddeley introduced the function J = (1 \Gamma G)=(1 \Gamma F ) as a measure of int...
One natural way to measure model adequacy is by using statistical distances as loss functions. A rel...
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...
Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics....
We focus on an important property upon generalization of the Kullback-Leibler divergence used in non...