Climate change impact assessment on groundwater level changes: A study of hybrid model techniques by Stephen Afrifa, Tao Zhang, Xin Zhao, Peter Appiahene, Mensah Samuel Yaw

“…The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It is applied research where monthly data collected from 2010 to 2022 were used. …”
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Assessing the dynamics of  Mycobacterium bovis infection in three French badger populations by Calenge, Clément, Payne, Ariane, Réveillaud, Édouard, Richomme, Céline, Girard, Sébastien, Desvaux, Stéphanie

“…Our first aim was to describe the dynamics of the infection in these clusters. We developed a Bayesian model of prevalence accounting for the spatial structure of the cases, the imperfect and variable sensitivity of the diagnostic tests, and the correlation of the infection status of badgers in the same commune caused by local factors (e.g., social structure and proximity to infected farms). …”
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Genetic structure of the collection of ryegrass (<i>Lolium</i>) cultivars: a study based on SSR and SCoT markers by Yu. M. Mavlyutov, E. A. Vertikova, A. O. Shamustakimova, I. A. Klimenko

“…Genetic relationships among the studied cultivars were analyzed on the basis of the Neighbor-Joining dendrogram and Bayesian model.Results. To assess the genetic polymorphism of ryegrass species and varieties, 7 SSR loci were selected, for which 110 allelic variants were identified (34 alleles were unique for individual cultivars), and 9 SCoT loci, for which 78 polymorphic amplification fragments were identified, with 28 being cultivar-specific. …”
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Image Recognition as a “Dialogic AI Partner” Within Biodiversity Citizen Science—an empirical investigation by Nirwan Sharma, Laura Colucci-Gray, Poppy Lakeman-Fraser, Annie Robinson, Julie Newman, René Van der Wal, Stefan Rueger, Advaith Siddharthan

“…Given the inherent need for convergence in decision-making within scientific processes such as species identification tasks, we augmented the dialogic process with a Bayesian model that unifies potentially divergent human and AI perspectives post collaboration to achieve a more accurate consensus decision than that achieved by either AI or citizens. …”
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Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China by Tiantian Tang, Yifan Wu, Yujie Li, Lexi Xu, Xinyi Shi, Haitao Zhao, Guan Gui

“…The MSES was then evaluated against the traditional Bayesian model averaging (BMA) approach. Our comprehensive evaluation, based on deterministic forecasting metrics such as the anomaly correlation coefficient (ACC), mean squared skill score (MSSS), and Graded Precipitation Score (Pg), demonstrated the MSES outperformed individual models and the BMA method. …”
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Geospatial mapping of drug-resistant tuberculosis prevalence in Africa at national and sub-national levels by Alemneh Mekuriaw Liyew, Archie C.A. Clements, Fasil Wagnew, Beth Gilmour, Kefyalew Addis Alene

“…Methods: We assembled a geolocated dataset from 173 sources across 31 African countries, comprising drug susceptibility test results and covariate data from publicly available databases. We used Bayesian model-based geostatistical framework with multivariate Bayesian logistic regression model to estimate DR-TB prevalence at lower administrative levels. …”
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Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin by Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen

“…This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. …”
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Value of Plasma NGAL and Creatinine on First Day of Admission in the Diagnosis of Cardiorenal Syndrome Type 1 by Hao Phan Thai, Bao Hoang Bui, Tien Hoang Anh, Minh Huynh Van

“…Building the optimal regression model (without eGFRCKDEPID1) by the BMA (Bayesian model average) method with two variables NGAL and Creatinine D1, we had the equation: odds ratio = ey while y = −2.39 + 0.0037 × NGAL + 0.17 × Creatinine D1. …”
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The dynamics of sea otter prey selection under population growth and expansion by Clinton B. Leach, Benjamin P. Weitzman, James L. Bodkin, Daniel Esler, George G. Esslinger, Kimberly A. Kloecker, Daniel H. Monson, Jamie N. Womble, Mevin B. Hooten

“…Specifically, we developed a multilevel Bayesian model to capture how sea otter diet at a location (the number, type, and size of prey collected) changed as a function of local cumulative otter abundance and the year in which the location was first occupied. …”
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Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field by Zhikai Yu, Binghao Yang, Penghu Wei, Hang Xu, Yongzhi Shan, Xiaotong Fan, Huaqiang Zhang, Changming Wang, Jingjing Wang, Shan Yu, Guoguang Zhao

“…The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. …”
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A novel model-based meta-analysis to indirectly estimate the comparative efficacy of two medications: an example using DPP-4 inhibitors, sitagliptin and linagliptin, in treatment o... by Yan Gong, James Rogers, Christian Friedrich, Sanjay Patel, Alexander Staab, Jorge Luiz Gross, Daniel Polhamus, William Gillespie, Brigitta Ursula Monz, Silke Retlich

“…Comparison of two oral dipeptidyl peptidase (DPP)-4 inhibitors, sitagliptin and linagliptin, for type 2 diabetes mellitus (T2DM) treatment was used as an example.Design Systematic review with MBMA.Data sources MEDLINE, EMBASE, http://www.ClinicalTrials.gov, Cochrane review of DPP-4 inhibitors for T2DM, sitagliptin trials on Food and Drug Administration website to December 2011 and linagliptin data from the manufacturer.Eligibility criteria for selecting studies Double-blind, randomised controlled clinical trials, ≥12 weeks’ duration, that analysed sitagliptin or linagliptin efficacies as changes in glycated haemoglobin (HbA1c) levels, in adults with T2DM and HbA1c &gt;7%, irrespective of background medication.Model development and application A Bayesian model was fitted (Markov Chain Monte Carlo method). …”
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Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined? by N. Besic, N. Picard, C. Vega, J.-D. Bontemps, L. Hertzog, J.-P. Renaud, J.-P. Renaud, F. Fogel, M. Schwartz, A. Pellissier-Tanon, G. Destouet, F. Mortier, F. Mortier, M. Planells-Rodriguez, P. Ciais

“…This article seeks to endorse the latter by utilizing the Bayesian model averaging approach to diagnose and interpret the differences between predictions from different models. …”
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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China by Xinzhi Wang, Qingxia Lin, Zhiyong Wu, Yuliang Zhang, Changwen Li, Ji Liu, Shinan Zhang, Songyu Li

“…Cropland has shifted from higher exposure to long-term drought to higher exposure to short-term, frequency drought. (3) Among the four machine learning models, the Bayesian model demonstrated superior performance in precipitation and temperature predictions, respectively, while the BiGRU model exhibited the best performance in long-term predictions of evaporation and soil moisture. (4) The central and southern regions will further increase in agricultural GDP exposure to both meteorological and agricultural droughts from 2021 to 2030, with exposures anticipated to increase by 20.2–34.8 % compared to the period from 2011 to 2020. …”
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PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy by Lovro Palaversa, Željko Ivezić, Neven Caplar, Karlo Mrakovčić, Bob Abel, Oleksandra Razim, Filip Matković, Connor Yablonski, Toni Šarić, Tomislav Jurkić, Sandro Campos, Melissa DeLucchi, Derek Jones, Konstantin Malanchev, Alex I. Malz, Sean McGuire, Mario Jurić

“…Anticipating photometric catalogs with tens of billions of stars from Rubin's Legacy Survey of Space and Time (LSST), we present a Bayesian model and pipeline that build on previous work and can handle LSST-sized datasets. …”
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I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations by Carolyn S Calfee, Michael A Matthay, Paul Henderson, Martin Eklund, Laura J Esserman, Chirag Patel, Angela Haczku, Michelle Jung, Tony Oliver, Alessio Crippa, Andrea Discacciati, Kathleen D Liu, Ellen L Burnham, Nuala J Meyer, Fady A Youssef, Derek W Russell, Julie Lang, Kevin W Gibbs, Daniel C Files, Sheetal Gandotra, Purnema Madahar, Nathan K Cobb, Timothy E Albertson, Rajiv Sonti, John P Reilly, Alejandra Jauregui, Iván García, Luis E Huerta, Daniel Clark Files, Neil R Aggarwal, Adam L Asare, Jeremy R Beitler, Paul A Berger, George Cimino, Melissa H Coleman, Paul T Henderson, Caroline A G Ittner, Kashif T Khan, Jonathan L Koff, Mary LaRose, Joe Levitt, Ruixiao Lu, Jeffrey D McKeehan, Karl W Thomas, Aaron M Mittel, Albert F Yen, Alexis E Suarez, Alexis L Serra, Alpesh N Amin, Amanda Rosen, Amy L Dzierba, Anna D Barker, Ariel R Weisman, Brian M Daniel, Brian M Morrissey, Caroline AG Ittner, Chayse Jones, Christina Creel-Bulos, Christina M Angelucci, Diana Ng, Fredy Chaparro-Rojas, Gavin H Harris, Harsh V Barot, Heny Su, Jacqueline B Sutter, Jamal Dodin, Jerry S Lee, John Kazianis, Joshua F Detelich, Julie E Lang, Justin Muir, Katarzyna Gosek, Katherine L Nugent, Kimberly Yee, Laura G Rodrigues, Laura R Macias, Lindsey A Orr, Lindsie L Boerger, Lissette Rosario-Remigio, Lucia Kufa, Maged Tanios, Maria B Reyes, Max W Adelman, Maya M Juarez, Michelle Meyers, Mitchell P Sternlieb, Neil Aggarwal, Nilam S Mangalmurti, Patrice Jones, Paul L Saban, Peter S Marshall, Philiip A Robinson, Philip Yang, Rahul Nair, Richard Anthony Lee, Richard G Wunderink, Romina Wahab, Roxana A Lupu, Santhi I Kumar, Sara C Auld, Scott Fields, Se Fum Wong, Skyler J Pearson, Spencer Whealon, Timothy F Obermiller, Anita Darmanian, John Schicchi, Esmeralda Martinez, Farjad Sarafian, Julie Nguyen, Bethany Weiler-Lisowski, Jaime Wyatt, Daniel Blevins, Marylee Melendrez, Brenda Lopez, Hiwet Tzehaie, Omowunmi Amosu, Austin Simonson, Erin Hardy, Brett Lindgren, Gregory Peterfreund, Leigha Landreth, Lisa Parks

“…The statistical design uses a Bayesian model with ‘stopping’ and ‘graduation’ criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. …”
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Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction by Esam Mahdi, Sana Alshamari, Maryam Khashabi, Alya Alkorbi

“…Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. …”
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Frequency-specific changes in prefrontal activity associated with maladaptive belief updating in volatile environments in euthymic bipolar disorder by Marina Ivanova, Ksenia Germanova, Dmitry S. Petelin, Aynur Ragimova, Grigory Kopytin, Beatrice A. Volel, Vadim V. Nikulin, Maria Herrojo Ruiz

“…Here, we integrated hierarchical Bayesian modelling with magnetoencephalography (MEG) to characterise maladaptive belief updating in this condition. …”
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Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage by Muluken Enyew, Mulatu Geleta, Kassahun Tesfaye, Amare Seyoum, Tileye Feyissa, Admas Alemu, Cecilia Hammenhag, Anders S. Carlsson

“…The genomic prediction accuracy estimated for the studied traits using five Bayesian models ranged from 0.30 to 0.63 while it ranged from 0.35 to 0.60 when the RR-BLUP model was used. …”
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A Bayesian Markov Framework for Modeling Breast Cancer Progression by Tong Wu

“…Parameters are estimated utilizing maximum likelihood estimation and Bayesian models with Gibbs sampling to ensure robustness and methodological rigor. …”
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