Exploring the genetic associations and causal relationships between antibody responses, immune cells, and various types of breast cancer by Yang Yang, Jiayi Chen, Fuhong Gong, Jingge Miao, Mengping Lin, Ruimin Liu, Chenxi Wang, Fei Ge, Wenlin Chen

“…However, none of these SNPs passed the Bayesian colocalization test by moloc. These results excluded horizontal pleiotropy, stabilizing MR analysis results. …”
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Unconstrained Metropolis–Hastings Sampling of Covariance Matrices by Daniel Turek

“…Markov chain Monte Carlo (MCMC), the predominant algorithm for fitting hierarchal models to data in a Bayesian setting, relies on the ability to sample from the full conditional distributions of unobserved parameters. …”
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Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany by Loukas Kyriakidis, Rushit Kansara, Maria Isabel Roldán Serrano

“…While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. …”
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Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control by Ties van derHeijden, Miguel Angel Mendoza‐Lugo, Peter Palensky, Nick van deGiesen, Edo Abraham

“…We utilize Combined Quantile Regression Deep Neural Networks and Non‐parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. …”
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Genetic Strategies for Enhancing Rooster Fertility in Tropical and Humid Climates: Challenges and Opportunities by Jiraporn Juiputta, Vibuntita Chankitisakul, Wuttigrai Boonkum

“…We addressed the trends and scientific developments in male chicken genetic selection, together with the benefits and constraints of each method. This will help breeders and researchers to create the most successful genetic selection plans for the next generation of chickens.…”
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Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach by Ehsan Esmaeeli, Mohsen Varmazyar, Vahid Hekmatshoar, Parviz Boroomandfar, Mohammad Reza Feylizadeh

“…The current study presents a comprehensive framework that integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Bayesian Best-Worst Method (BWM), and a multi-dimensional knapsack optimization model to address maintenance backlog management challenges. …”
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Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China by Yong Bo, Xueke Li, Kai Liu, Shudong Wang, Dehui Li, Yu Xu, Mengmeng Wang

“…Abstract Current irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. …”
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A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents by Ricardo Scheufen Tieghi, José Teófilo Moreira-Filho, Holli-Joi Martin, James Wellnitz, Miguel Canamary Otoch, Marielle Rath, Alexander Tropsha, Eugene N. Muratov, Nicole Kleinstreuer

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Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations by Y. Liu, Y. Liu, I. S. Adams

“…Starting with an initial assumption, the algorithm iteratively refines the 2D cloud microphysical quantities by minimizing discrepancies between TB simulations and observations under prior constraints. Both tomographic algorithms are developed based on a hybrid of Bayesian Monte Carlo integration (MCI) and the optimal estimation method (OEM). …”
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Design of an Iterative AI-Driven Latency Prediction and QoS-Aware Task Scheduling in Mobile Edge Computing: A Federated and Reinforcement Learning Process by Sharma Garima, Khare Rashi, Kulkarni Neha, Pagare Sanjay, Tiwari Vanshika

“…Lastly, Multi-Agent Reinforcement Learning with Meta-Learning (MARL-Meta) refines task scheduling by dynamically adjusting policies with respect to predicted latencies, task priorities, and constraints of MEC resources. This synchronized AI-based framework achieves a 74.4% gain in reducing latency prediction error, 35% enhancement in task execution time, 67.5% decline in task failure rates, and 30.6% increase in resource utilization when compared to conventional MEC scheduling methods. …”
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Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection by Thomas Kinyanjui Njoroge, Kelvin Mugoye Shindu, Rachael Kibuku

“…Traditional diagnostic methods, such as manual inspections, are often inefficient and error-prone. …”
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A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks by Abdallah Ghourabi, Adel Alkhalil

“…The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. …”
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Real‐Time Behaviour Recognition on Bio‐Loggers Enables Autonomous Audio Playback Experiments in Free‐Ranging Seabirds by Ryoma Otsuka, Hibiki Sugiyama, Yuichi Mizutani, Ken Yoda, Takuya Maekawa

“…By removing the location constraints of traditional playback experiments, the system allows a variety of playback experiments to be tested in various situations. …”
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Polka-dotted Stars: A Hierarchical Model for Mapping Stellar Surfaces Using Occultation Light Curves and the Case of TOI-3884 by Sabina Sagynbayeva, Will M. Farr, Brett M. Morris, Rodrigo Luger

“…We present StarryStarryProcess , a novel hierarchical Bayesian framework for mapping stellar surfaces using exoplanet transit light curves. …”
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A Next-generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM,... by Tonmoy Deka, Tasneem Basra Khan, Swastik Dewan, Priyankush Ghosh, Debayan Das, Liton Majumdar

“…This hybrid approach enables a comparison between traditional Bayesian methods and computationally efficient machine learning techniques. …”
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Excess Ground Ice Profiles in Continuous Permafrost Mapped From InSAR Subsidence by S. Zwieback, G. Iwahana, S. Sakhalkar, R. Biessel, S. Taylor, F. J. Meyer

“…Here, we introduce a Bayesian inversion method based on remotely sensed subsidence. …”
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The MASSIVE Survey. XX. A Triaxial Stellar Dynamical Measurement of the Supermassive Black Hole Mass and Intrinsic Galaxy Shape of Giant Radio Galaxy NGC 315 by Jacob Pilawa, Emily R. Liepold, Chung-Pei Ma, Jonelle L. Walsh, Jenny E. Greene

“…Using ∼2300 kinematic constraints, we perform triaxial stellar orbit modeling with the TriOS code and search over ∼15,000 galaxy models with a Bayesian scheme to simultaneously measure six mass and intrinsic shape parameters. …”
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Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State by Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng, John Veitch

“…However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. …”
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Evaluation of diverse polygenic risk score models for lung cancer in a small-scale Chinese cohort by Min Gao, Min Gao, Min Gao, Qiwen Zheng, Qiwen Zheng, Yue Jiang, Xiao Chang, Xiao Chang, Xin Zheng, Xin Zheng

“…We assessed (i) a previously reported 19-SNP PRS developed in Chinese individuals, (ii) genome-wide PRS derived using PRS-CS with East Asian and European GWAS summary statistics, and (iii) PRS-CSx, a cross-population Bayesian framework that integrates summary statistics across ancestries.ResultsThe 19-SNP PRS demonstrated limited discriminative power in our cohort. …”
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Present-Day Tectonic Deformation Characteristics of the Northeastern Pamir Margin Constrained by InSAR and GPS Observations by Junjie Zhang, Xiaogang Song, Donglin Wu, Xinjian Shan

“…Based on the two-dimensional screw dislocation model and using the Bayesian Markov chain Monte Carlo (MCMC) inversion method, the kinematic parameters of the fault were calculated, revealing the fault kinematic characteristics in this region. …”
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