Results 1 to 10 of about 2,511,094 (193)
Ensemble Estimation of Information Divergence † [PDF]
Entropy, 2018 Recent work has focused on the problem of nonparametric estimation of information divergence functionals between two continuous random variables. Many existing approaches require either restrictive assumptions about the density support set or difficult ...
Kevin R. Moon +3 more
doaj +8 more sources
Statistical Estimation of the Kullback–Leibler Divergence [PDF]
Mathematics, 2021 Asymptotic unbiasedness and L2-consistency are established, under mild conditions, for the estimates of the Kullback–Leibler divergence between two probability measures in Rd, absolutely continuous with respect to (w.r.t.) the Lebesgue measure.
Alexander Bulinski, Denis Dimitrov
doaj +5 more sources
Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation. [PDF]
Entropy (Basel), 2022 Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data.
Li CJ +4 more
europepmc +2 more sources
Divergence Estimation in Message Passing Algorithms [PDF]
IEEE Transactions on Information Theory, 2023 Many modern imaging applications can be modeled as compressed sensing linear inverse problems. When the measurement operator involved in the inverse problem is sufficiently random, denoising Scalable Message Passing (SMP) algorithms have a potential to demonstrate high efficiency in recovering compressed data.
Nikolajs Skuratovs, Mike E. Davies 0001
openaire +2 more sources
BMC Ecology and Evolution, 2021 Background Divergence time estimation is fundamental to understanding many aspects of the evolution of organisms, such as character evolution, diversification, and biogeography.
De Chen +6 more
doaj +2 more sources
Uncertainty in Divergence Time Estimation [PDF]
Systematic Biology, 2020 Abstract Understanding and representing uncertainty is crucial in academic research because it enables studies to build on the conclusions of previous studies, leading to robust advances in a particular field. Here, we evaluate the nature of uncertainty and the manner by which it is represented in divergence time estimation, a field that
Carruthers, T, Scotland, RW
openaire +4 more sources
Minimum Penalized ϕ-Divergence Estimation under Model Misspecification [PDF]
Entropy, 2018 This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters.
M. Virtudes Alba-Fernández +2 more
doaj +2 more sources
CladeDate: Calibration information generator for divergence time estimation
Methods in Ecology and Evolution, 2022 Time‐scaled phylogenetic trees are essential tools in modern biology and node‐based calibrations have been the main approach to time‐tree estimation. But methods for generating the required calibration information are scarce and difficult to parameterize.
Santiago Claramunt
doaj +2 more sources
Density estimation with minimization of U-divergence [PDF]
Machine Learning, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kanta Naito +2 more
exaly +2 more sources
Signal Processing, 2020 Most data-driven diagnosis methods that are designed to detect faults, rely on measuring the mean and variation shifts. However, for incipient fault detection, these statistical criteria are slightly varying and are difficult to be accurately evaluated ...
, Claude Delpha
exaly +2 more sources