Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies by Sanni Översti, Ariane Weber, Viktor Baran, Bärbel Kieninger, Alexander Dilthey, Torsten Houwaart, Andreas Walker, Wulf Schneider-Brachert, Denise Kühnert

“…For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. …”
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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition by Yihang Zhang, Hang Zhao

“…Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh–Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. …”
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Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty by Qinghua Wei, Yuanhao Wang, Guo Yang, Tianyuan Li, Shuting Yu, Ziqiang Dong, Tong-Yi Zhang

“…The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. …”
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Testing source elevation versus genotype as predictors of sugar pine performance in a post‐fire restoration planting by Emily V. Moran, Rainbow DeSilva, Courtney Canning, Jessica W. Wright

“…Of these, 323 either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings in a separate post‐fire planting experiment, to test the relative predictive ability of source elevation (a common proxy for source climate) versus the proportion of seedling alleles expected to be locally advantageous based on GEA. …”
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Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem by Stefan Michael Stroka, Christian Heumann

“…This approach, applied with both linear and Bayesian linear regression, aims to enhance supervised learning models. …”
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Reinforcement Learning-Based Augmentation of Data Collection for Bayesian Optimization Towards Radiation Survey and Source Localization by Jeremy Marquardt, Leonard Lucas, Stylianos Chatzidakis

“…Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the number of trials. …”
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Detecting Schistosoma infections in endemic countries: a diagnostic accuracy study in rural Madagascar by Eva Lorenz, Ravo Razafindrakoto, Pia Rausche, Zaraniaina Tahiry Rasolojaona, Nantenaina Matthieu Razafindralava, Alexandre Zerbo, Yannick Höppner, Heidrun von Thien, Njary Rakotozandrindrainy, Cheick Oumar Doumbia, Philipp Klein, Jean-Marc Kutz, Paul L. A. M. Corstjens, Claudia de Dood, Pytsje T. Hoekstra, Govert J. van Dam, Anna Jaeger, Norbert Georg Schwarz, Egbert Tannich, Mala Rakoto Andrianarivelo, Raphael Rakotozandrindrainy, Rivo Andry Rakotoarivelo, Jürgen May, Tahinamandranto Rasamoelina, Daniela Fusco

“…Bayesian latent class models were used to assess diagnostic accuracies and disease prevalence. …”
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Leveraging Bayesian optimization and multilayer artificial neural network (MLANN) for fault prediction in oil-immersed transformers by Elahe Moradi

“…The proposed multilayer artificial neural network model, optimized using the Bayesian optimization method, achieved an outstanding accuracy of 97.99 %, pointedly outperforming benchmark machine learning classifiers. …”
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Value of APACHE II, SOFA and CardShock scoring as predictive tools for cardiogenic shock: A single‐centre pilot study by Małgorzata Chlabicz, Wojciech Łaguna, Remigiusz Kazimierczyk, Ewelina Kazimierczyk, Paulina Łopatowska, Monika Gil, Bożena Sobkowicz, Karol A. Kamiński, Agnieszka Tycińska

“…The Bayesian Weibull model demonstrated the utility of all scales in estimating short‐term risk in patients with CS, with the impact of APACHE II and SOFA on patient life expectancy decreasing to a non‐significant level at approximately 32 days and CardShock at 33 days. …”
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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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Stability of f(Q, B) Gravity via Dynamical System Approach: A Comprehensive Bayesian Statistical Analysis by Santosh V. Lohakare, B. Mishra

“…Additionally, we utilize center manifold theory to examine the stability of this critical point, providing deeper insights into the behavior of the model. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

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Probabilistic Power Flow Analysis of DERs Integrated Power System From a Bayesian Parameter Estimation Perspective by Wanjoli, Paul, Moustafa, Mohamed M.Zakaria, Abbasy, Nabil H.

“…The rise of distributed energy resources (DERs) in power systems demands efficient models for power flow analysis. Existing models often face challenges in balancing computation speed and accuracy. …”
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COVID-19 Vaccination and Cardiovascular Events: A Systematic Review and Bayesian Multivariate Meta-Analysis of Preventive Benefits and Risks by Raheleh Karimi, Mina Norozirad, Foad Esmaeili, Marjan Mansourian, Hamid R. Marateb

“…Markov chain Monte Carlo (MCMC) methods were detected for easy implementation of the Bayesian approach. Also, the sensitivity analysis of the model was done by using different priors. …”
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Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution by Ahlam H. Tolba, Ahmed Ramses El-Saeed

“…Both classical and Bayesian approaches facilitate point- and interval-estimation procedures for parameters and parametric functions. …”
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A Bayesian quantile regression approach for determining risk factors of low birth weight of under five children in Cambodia by Md. Moyazzem Hossain

“…Multivariable simultaneous quantile regression models in a Bayesian setting were used to determine the factors associated with Cambodian children’s low birth weight. …”
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Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

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Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains by Mingdong Li, Shuhang Chen, Xiang Zhang, Yiwen Wang

“…In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. …”
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Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series by M. Bottani, M. Bottani, M. Bottani, M. Bottani, L. Ferro-Famil, L. Ferro-Famil, J. Doblas, S. Mermoz, A. Bouvet, T. Koleck, T. Koleck

“…Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. …”
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Cross-Temporal Remote Sensing Image Change Captioning: A Manifold Mapping and Bayesian Diffusion Approach for Land Use Monitoring by Qingshan Bai, Xiaohua Wang

“…This study proposes a cross-temporal remote sensing image change captioning (RSICC) model named CTM, which is constructed based on manifold mapping and Bayesian diffusion techniques. …”
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