Radiation divergence, which is an important reason for dynamical forcing, is dominated by the cloud distribution in the atmosphere.
In the model, an item similarity measure is designed based on the Kullback Leibler (KL) divergence, which is used as a weight to correct the output of an adjusted Proximity Significance Singularity model.
Information gain (IG) is an alternative synonym for Kullback Leibler divergence, which is frequently employed in the field of machine learning, especially in text categorization [ 19].
In fact ion energy spectra have exponential trend, almost 100% energy spread and a wide angular divergence which is the biggest issue in the beam transport and, hence, in a wider use of this technology.
This approach-to-equilibrium kinetic model can be applied to the analysis of nucleotide sequence divergence, which is no more (and no less) than a chemical reaction interconverting two chemical states.
The basic IVA framework was originally introduced in [13, 14], this method used Kullback-Leibler (KL) divergence, which is completely equivalent to mutual information of the sources, as an objective function, with a natural gradient-based updating rule to optimize the demixing matrices frequency bin-by-frequency bin.
Studies that experimentally support the idea of overdominance in allele numbers [e.g. [ 26], up to an optimum in [ 27]], may in fact show indirectly the effect of allele divergence, which is supported by our observation that the genetic divergence within an individual correlates strongly with the number of alleles.
Indeed, we observe a strong anti-correlation between synonymous diversity and nonsynonymous divergence, which is further quantified in Figure 5C.
The simulator proposed in[51] is based on Kullback-Leibler divergence which is compared with IFFT based approach of simulator design.
Chaotic functions are characterized by their fast divergence, which is more well known as the remarkable butterfly effect.
