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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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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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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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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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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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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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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Multi-state models: metapopulation and life history analyses by A. N. Arnason, E. Cam

“…Our call for papers for this session also invited papers illustrating multi– population meta–analysis and use of Bayesian methods. By these criteria, their paper is no outlier. …”
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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

“…Abstract Background: There may be potential associations between various pathogens, antibody immune responses, and breast cancer (BC), but the specific mechanisms and causal relationships remain unclear. Methods: First, multiple Mendelian randomization (MR) methods were used for univariable MR analysis to explore potential causal relationships between 34 antibody immune responses (related to 12 pathogens), 46 antibody immune responses (related to 13 pathogens), antibody responses post-COVID-19 vaccination, 731 immune cell types, and various BC subtypes (including overall BC, ER-positive, ER-negative, Luminal A, Luminal B, Luminal B HER2-negative, HER2-positive, and triple-negative BC). …”
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Multi-criteria pressure sensors placement in water distribution networks using fuzzy TOPSIS by Mohammad Mehdi Riyahi, Carlo Giudicianni, Amin E. Bakhshipour, Ali Haghighi, Enrico Creaco

“…These candidate nodes are then fed into the new optimization algorithm, called the binary genetic-differential evolutionary algorithm (BGDE), to find the optimal nodes, which are then sorted using the fuzzy TOPSIS method. The BGDE considers sensitivity and entropy as objective functions, while the investment cost is taken into account as a constraint. …”
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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

“…Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. …”
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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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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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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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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

“…In the future, the system can be extended to stimulate other sensor modalities (e.g., magnetic sensors), expanding the possibilities for intervention methods in the wild environment.…”
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Handling Censoring and Censored Data in Survival Analysis: A Standalone Systematic Literature Review by Anthony Joe Turkson, Francis Ayiah-Mensah, Vivian Nimoh

“…These methods include censored network estimation; conditional mean imputation method; inverse probability of censoring weighting; maximum likelihood estimation; Buckley-Janes least squares algorithm; simple multiple imputation strategy; filter algorithm; Bayesian framework; β-substitution method; search-and-score-hill-climbing algorithm and constraint-based conditional independence algorithm; frequentist; Markov chain Monte Carlo for imputed data; quantile regression; random effects hierarchical Cox proportional hazards; Lin’s Concordance Correlation Coefficient; classical maximum likelihood estimate. …”
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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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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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