Results 131 to 140 of about 59,513 (222)

A Maximum Value for the Kullback–Leibler Divergence between Quantized Distributions

Information
The Kullback–Leibler (KL) divergence is a widely used measure for comparing probability distributions, but it faces limitations such as its unbounded nature and the lack of comparability between distributions with different quantum values (the discrete ...
Vincenzo Bonnici
doaj   +1 more source

Text Mining in Bibliometrics and Science Mapping: A Methodological Review

WIREs Computational Statistics, Volume 18, Issue 2, June 2026.
Text mining has become a foundational component of contemporary bibliometrics and science mapping, enabling systematic analysis of the semantic structure, thematic evolution, and cognitive organization of scientific fields. Integrating textual evidence with relational indicators enriches knowledge maps and supports more comprehensive, content‐sensitive
Michelangelo Misuraca
wiley   +1 more source

Orthogonal nonnegative matrix factorization with the Kullback–Leibler divergence

Pattern Recognition Letters
10 pages, corrected some ...
Nkurunziza, Jean Pacifique, Nahayo, Fulgence, GILLIS, Nicolas   +2 more
openaire   +3 more sources

Distribution‐Guided Ensemble Postprocessing for S2S Precipitation Forecasts: A Seamless Pathway Using Deep Generative Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Atmosphere‐ocean‐land coupled forecasting systems, despite their comprehensiveness, face substantial challenges in the “predictability desert” at subseasonal to seasonal (S2S) timescales, particularly for precipitation—a variable crucial for socioeconomic activities yet of stunning spatiotemporal variance. Post‐processing methods developed for
Wen Shi, Baoxiang Pan, Jianbin Huang, Tingfeng Dou, Jie Feng, Huihui Yuan, Anran Wang, Xin Xia, Lei Huang, Yong Luo   +9 more
wiley   +1 more source

A Probabilistic Greedy Attempt to Be Fair in Neural Team Recommendation

Computational Intelligence, Volume 42, Issue 3, June 2026.
ABSTRACT Neural team recommendation has brought state‐of‐the‐art efficacy while enhancing efficiency at forming teams of experts whose success in completing complex tasks is almost surely guaranteed. However, they overlook fairness, that is, predicted teams are heavily biased toward popular and male experts, falling short of recommending female or ...
Hamed Loghmani, Mahdis Saeedi, Gabriel Rueda, Edwin Paul, Hossein Fani   +4 more
wiley   +1 more source

Model Ambiguity versus Model Misspecification in Dynamic Portfolio Choice

The Journal of Finance, Volume 81, Issue 3, Page 1741-1795, June 2026.
ABSTRACT We study aversion to model ambiguity and misspecification in dynamic portfolio choice. Risk‐averse investors (relative risk aversion γ>1$\gamma > 1$) fear return persistence, while risk‐tolerant investors (0<γ<1$0<\gamma <1$) fear mean reversion, when confronting model misspecification concerns of identically and independently distributed (IID)
PASCAL J. MAENHOUT, HAO XING, ANNE G. BALTER   +2 more
wiley   +1 more source

An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 9, 16 May 2026.
Abstract Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure.
Samson Hagos, Zhe Feng, Adam C. Varble, Sheng‐Lun Tai   +3 more
wiley   +1 more source

Efficient Deconvolution in Populational Inverse Problems

International Journal for Numerical Methods in Engineering, Volume 127, Issue 9, 15 May 2026.
ABSTRACT This work is focused on the inversion task of inferring the distribution over parameters of interest, leading to multiple sets of observations. The potential to solve such distributional inversion problems is driven by the increasing availability of data, but a major roadblock is blind deconvolution, arising when the observational noise ...
Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart   +2 more
wiley   +1 more source

Identifying critical state of complex diseases by single-sample Kullback-Leibler divergence. [PDF]

BMC Genomics, 2020
Zhong J, Liu R, Chen P.
europepmc   +1 more source

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, Volume 13, Issue 27, 13 May 2026.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source