Erratum: Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization. [Med. Phys. 46(1), p. 81-92 (2019)]. [PDF]
Med Phys, 2020 Ha W +4 more
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Coarse‐to‐Fine Spatial Modeling: A Scalable, Machine‐Learning‐Compatible Framework
Geographical Analysis, Volume 58, Issue 2, April 2026.ABSTRACT This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models.
Daisuke Murakami +5 more
wiley +1 more source
Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification [PDF]
, 2019 Gongqing Wu +5 more
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Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. [PDF]
Eur J Nucl Med Mol Imaging, 2020 Wang M +10 more
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Parameter identification in Choquet Integral by the Kullback-Leibler divergence on continuous densities with application to classification fusion [PDF]
, 2011 Emmanuel Ramasso, S. Jullien
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Statistical Incipient Fault Detection and Diagnosis with Kullback-Leibler Divergence : from Theory to Applications [PDF]
, 2014 Jinane Harmouche
openalex
MMSE Bounds Under Kullback-Leibler Divergence Constraints on the Joint Input-Output Distribution [PDF]
, 2020 Michael Fauß +2 more
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Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization. [PDF]
Med Phys, 2019 Ha W +4 more
europepmc +1 more source
Entropy and the Kullback–Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation [PDF]
Marco Scutari
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