Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference by Ming WU, Tie-cheng SONG, Jing HU, Lian-feng SHEN

“…Then, an estimator of mod-el coefficient vector was designed by utilizing the th of variational Bayesian inference (VBI). Simulation results show that the proposed approximate model has good accuracy, and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. …”
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Distributionally Robust Variational Quantum Algorithms With Shifted Noise by Zichang He, Bo Peng, Yuri Alexeev, Zheng Zhang

“…Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. …”
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Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions. by Tomáš Gavenčiak, Joshua Teperowski Monrad, Gavin Leech, Mrinank Sharma, Sören Mindermann, Samir Bhatt, Jan Brauner, Jan Kulveit

“…Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. …”
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Bayesian Estimation of Individual Gray Whale Space Use Reveals Differential Exposure to Stressors by Lisa Hildebrand, Leslie New, Enrico Pirotta, Joshua D. Stewart, Ines Hildebrand, Carrie Newell, K. C. Bierlich, Clara N. Bird, Alejandro Fernandez Ajó, Daniel Turek, Leigh G. Torres

“…ABSTRACT This study quantifies the individual space use patterns of Pacific Coast Feeding Group gray whales (Eschrichtius robustus) from photographic capture‐recapture data, collected in central Oregon, U.S.A., within a Bayesian framework. We evaluate the potential exposure of individuals to six anthropogenic stressors given their space use patterns. …”
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Influence of multiple network structures on bayesian estimation of peer effects and statistical power for generalized linear network autocorrelation models by Guanqing Chen, A. James O’Malley

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Identifying spatiotemporal variations in four-spot megrim (Lepidorhombus boscii) fishery nurseries to enhance management and stock assessment by Esther Abad, Francisco Izquierdo, Jorge Landa, Francisco Velasco, Manuel Hidalgo, Maria Grazia Pennino

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Decoding cortical chronotopy—Comparing the influence of different cortical organizational schemes by Falko Mecklenbrauck, Jorge Sepulcre, Jana Fehring, Ricarda I. Schubotz

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Preparing to act follows Bayesian inference rules by Luca Tarasi, Chiara Tabarelli de Fatis, Margherita Covelli, Giuseppe Ippolito, Alessio Avenanti, Vincenzo Romei

“…Notably, drift-diffusion modeling revealed that trial-by-trial MEPs variations significantly influenced decision-making bias. …”
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Expectancy-based rhythmic entrainment as continuous Bayesian inference. by Jonathan Cannon

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Spatiotemporal Characteristics and Influencing Factors of PM_2.5 Levels in Lianyungang: Insights from a Multidimensional Analysis by Xue Li, Haihong He, Dewei Wang, Wenli Qiao, Chunli Liu, Yiming Sun, Lulu Li, Shuting Han, Guozhen Zha

“…This study focused on Lianyungang, an industrial city, to analyze the spatiotemporal variations in PM_2.5 concentrations from 2000 to 2023 and identify the influencing factors. …”
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The Influence of Habitat Heterogeneity and Human Disturbance on Trophic Interactions of Sebastiscus marmoratus and Scorpaenopsis cirrosa in Wanshan Archipelago by Hongyu Xie, Yu Liu, Teng Wang, Peng Wu, Yayuan Xiao, Chunling Wang, Jian Zou, Yong Liu, Jinhui Sun, Jianzhong Shen, Xuefu Ao, Yanqiao Wang

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The candidate gene OLFML2A possibly contributed to the variation of total number of teats in Meishan and Erhualian pigs by influencing the formation of mammary placodes by C.X. Liu, R.H. Huang, W.D. Zhou, N.J. Jiang, Q. Liu, J.F. Ma, P.H. Li, Q.B. Zhao

“…After integrating multiple omics, we have tentatively identified the OLFML2A gene as a potential causal gene responsible for teat number variation in Meishan and Erhualian pigs. This gene could potentially influence the development of mammary placodes in Meishan and Erhualian pigs, consequently influencing the phenotype of teat number.…”
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Data reconstruction from machine learning models via inverse estimation and Bayesian inference by Agus Hartoyo, Dominika Ciupek, Maciej Malawski, Alessandro Crimi

“…Specifically, we derive expressions that quantify how variations in key variables influence the divergence between true and estimated posteriors by examining the concurrent behavior of their partial derivatives with respect to independent variables. …”
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Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework by Ayele Tesema Chala, Mais Mayassah, Clara Beatrice Vilceanu, Richard Ray

“…Accurate prediction of peak ground intensity measures is inevitably influenced by geotechnical variability. Variations in soil properties, subsurface conditions, and seismic inputs introduce complexities that challenge the reliability of predictions. …”
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Bayesian geo-additive model to analyze spatial pattern and determinants of maternal mortality in Ethiopia by Yitagesu Eshetu, Tigist Getachew

“…The Afar, Somali, Benishangul Gumuz, and Gambela regions have higher rates of maternal death, according to evidence of geographic variation in a model. Conclusion The findings of the study revealed that maternal mortality is influenced by numerous social, demographic, and geographic variables. …”
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Classical Versus Bayesian Error-Controlled Sampling Under Lognormal Distributions with Type II Censoring by Huasen Zhou, Wenhao Gui

“…The analysis focuses on how variations in prior distributions, specifically the beta distribution for defect rates, influence the producer’s and consumer’s risks, along with the optimal sample size. …”
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Bayesian network analysis of individual-level factors associated with bullying among high school students by Yanmei Feng, Xinyu Luo, Xinrui Li, Yilin Liu, Yong Zeng

“…The data were analyzed using chi-square tests, multifactor logistic regression, and Bayesian network models. Results Gender, stress perception, core self-evaluation, the dual mode of self-control, and depressive disorders were statistically significant factors associated with variations in bullying detection rates. …”
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Bayesian spatio-temporal conditional autoregressive localized modeling techniques for socioeconomic factors and stunting in Indonesia by Aswi Aswi, Septian Rahardiantoro, Anang Kurnia, Bagus Sartono, Dian Handayani, Nurwan Nurwan

“…Stunting remains a persistent public health issue in Indonesia, exhibiting significant spatial and temporal variation. To address this, we employed a hierarchical Bayesian spatio-temporal localized Conditional Autoregressive (CAR) model that includes a clustering component to identify risk factors and estimate relative risk (RR) across 34 provinces from 2020 to 2022. …”
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Complex Variation in Afrotropical Mammal Communities With Human Impact by Deogratias Tuyisingize, Lars Kulik, Delagnon Assou, Diorne Zausa, Solange Kamga, Onella Mundi, Stefanie Heinicke, Inza Kone, Samedi Jean Pierre Mucyo, Tenekwetche Sop, Christophe Boesch, Colleen Stephens, Anthony Agbor, Samuel Angedakin, Emma Bailey, Mattia Bessone, Charlotte Coupland, Josephine Head, Tobias Deschner, Paula Dieguez, Villard Ebot Egbe, Anne‐Céline Granjon, Thurston Cleveland Hicks, Sorrel Jones, Ammie K. Kalan, Kevin E. Langergraber, Juan Lapuente, Kevin C. Lee, Laura K. Lynn, Nuria Maldonado, Maureen S. McCarthy, Amelia Meier, Lucy Jayne Ormsby, Alex K. Piel, Lilah Sciaky, Volker Sommer, Fiona A. Stewart, Erin G. Wessling, Jane Widness, Roman M. Wittig, Pauline Strohbach, Mimi Arandjelovic, Yntze van derHoek, Hjalmar S. Kühl

“…ABSTRACT The diversity and composition of mammal communities are strongly influenced by human activities, though these relationships may vary across broad scales. …”
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Fatigue Reliability Analysis of Motor Hanger for High-Speed Train Based on Bayesian Updating and Subset Simulation by Yonghua Li, Pengpeng Zhi, Yue Zhang, Bingzhi Chen, Yuedong Wang

“…In order to more accurately analyze the fatigue reliability of motor hanger for high-speed train and reduce the influence of uncertain factors, a Bayesian statistical method is introduced to propose a novel fatigue reliability analysis method based on Bayesian updating and subset simulation. …”
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