Kullback-Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information. [PDF]
Sensors (Basel), 2019 Gao R, Zhang J, Xiao W, Li Y.
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Why Interest Groups With Divergent Goals Collaborate: Evidence From Climate Regulation
Economics &Politics, Volume 38, Issue 1, Page 46-61, March 2026.ABSTRACT Why do interest groups with contrasting interests and policy goals work together? I present a theory of collaborative policy production and show that interest groups can achieve higher policy gains through collaboration, even though their ideal policy goals may diverge significantly.
Dahyun Choi
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Kullback-Leibler divergence and the Pareto-Exponential approximation. [PDF]
Springerplus, 2016 Weinberg GV.
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A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes
Journal of Time Series Analysis, Volume 47, Issue 2, Page 304-320, March 2026.ABSTRACT This study considers the stationary higher‐order Markov process for circular data by employing the mixture transition distribution modeling. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models.
Hiroaki Ogata, Takayuki Shiohama
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Scandinavian Journal of Statistics, Volume 53, Issue 1, Page 364-394, March 2026.ABSTRACT Latent Gaussian models (LGMs) are a subset of Bayesian Hierarchical models where Gaussian priors, conditional on variance parameters, are assigned to all effects in the model. LGMs are employed in many fields for their flexibility and computational efficiency. However, practitioners find prior elicitation on the variance parameters challenging
Luisa Ferrari, Massimo Ventrucci
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Minimising the Kullback-Leibler Divergence for Model Selection in Distributed Nonlinear Systems. [PDF]
Entropy (Basel), 2018 Cliff OM, Prokopenko M, Fitch R.
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Geophysical Research Letters, Volume 53, Issue 4, 28 February 2026.Abstract The probability density function of drops is difficult to model. Current approaches make assumptions that are often problematic, as they allow negative values for the mean of the distribution. While the statistical goodness of fit of those models might be reasonable for precipitation radar estimation, the situation is unsatisfactory if a fully
Francisco J. Tapiador +9 more
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Kullback-Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking. [PDF]
Entropy (Basel), 2018 Hu C, Lin H, Li Z, He B, Liu G.
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Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling. [PDF]
BMC Med Imaging, 2017 Liang S +5 more
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ESTIMATION OF THE KULLBACK-LEIBLER DIVERGENCE
, 2003 The Kullback-Leibler (KL) divergence K(fl, P) between a set O of probability measures (PMs) on ]R d and some PM P cannot be estimated by K(O, Pn) when O contains PM whose support is not included in the support of the empirical measure Pn. We propose an estimation procedure which avoids any smoothing of Pn.
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