Results 141 to 150 of about 719,028 (211)

The similarity of degree distributions with the target degree distribution, as measured by the Jensen–Shannon divergence.

The similarity of degree distributions with the target degree distribution, as measured by the Jensen–Shannon divergence.
Jiaqi Wen (6838376), Bogdan Gabrys (11027595), Katarzyna Musial (8232552)   +2 more
core   +1 more source

Not all Jensen-Shannon Divergence Estimators are Equal

The Jensen-Shannon divergence is widely reported as a scalar measure of fidelity for synthetic tabular data. Yet, in practice, it is estimated from finite samples using protocols that are often underspecified. This creates a measurement problem. Although the population divergence is well defined, the empirical value depends on the estimator family ...
Garrido, Alba, Almodóvar, Alejandro, Elizo, Mar, Apellániz, Patricia A., Zazo, Santiago, Parras, Juan   +5 more
openaire   +2 more sources

Analysis of symbolic sequences using the Jensen-Shannon divergence

, 2020
We study statistical properties of the Jensen-Shannon divergence D, which quantifies the difference between probability distributions, and which has been widely applied to analyses of symbolic sequences.
Pedro Bernaola-Galván, Jose Oliver, Ivo Grosse, H Eugene Stanley, Pedro Carpena, Ramón Román-Roldán   +5 more
core  

Sample complexity bounds for the Jensen-Shannon divergence

The Jensen-Shannon divergence (JSD) is a symmetric and bounded measure of the dissimilarity of two probability distributions, which has become a standard tool in statistics, information theory, and machine learning. We complement the understanding of its mathematical properties by presenting an analysis of the amount of data that is needed to ...
Richter, Oren, Ben-Ari, Adi, Talpir, Tom, Schneidman, Elad   +3 more
openaire   +2 more sources

JS-Drift: A reproducible Jensen-Shannon divergence procedure for drift-aware client weighting in federated learning. [PDF]

MethodsX
A S, Arumugam S, A E.
europepmc   +1 more source

Universal Super-Tensorization of Jensen-Shannon Divergence

For any discrete memoryless channel W with equal support condition, we prove that lim(1 − η_JSD(W^⊗n))^{1/n} = BC_min(W), where BC_min is the minimum Bhattacharyya coefficient between output distributions. This establishes that bounded divergences exhibit super-tensorization (η → 1) with rate governed by the Chernoff exponent.
openaire   +2 more sources

The Blind Spot of Small-Sided Games: Missing High-Intensity Deceleration Demands in Elite Soccer Players. [PDF]

Sports (Basel)
Manzi V, Di Lascio G, Taleb M, Giardullo G, Laterza F, Savoia C, Caminiti G, D'Onofrio R, Raiola G.   +8 more
europepmc   +1 more source

Fourier Evaluation of Tracings and Acidosis in Labor (FETAL) Framework: An In-Silico Evaluation of a Spectral-Divergence Method. [PDF]

Cureus
Balayla J.
europepmc   +1 more source

Delay-Induced Hopf Bifurcation and Entropy-Based Distributional Uncertainty in a Stochastic Time-Delay Pheromone Feedback Model of Ant Foraging Dynamics. [PDF]

Entropy (Basel)
Zhu J, Wang L, Wang Q.
europepmc   +1 more source