Results 1 to 10 of about 7,662,414 (297)

Bayesian hierarchical models and prior elicitation for fitting psychometric functions [PDF]

Frontiers in Computational Neuroscience, 2023
Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti, Colleen P. Ryan, Colleen P. Ryan, Priscilla Balestrucci, Francesco Lacquaniti, Francesco Lacquaniti, Alessandro Moscatelli, Alessandro Moscatelli   +7 more
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Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models [PDF]

Frontiers in Psychology, 2022
Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information ...
Endris Assen Ebrahim, Endris Assen Ebrahim, Mehmet Ali Cengiz   +2 more
doaj   +2 more sources

Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models [PDF]

Heliyon
One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies ...
Irving Gómez-Méndez, Chainarong Amornbunchornvej   +1 more
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Bayesian hierarchical models for disease mapping applied to contagious pathologies. [PDF]

PLoS ONE, 2021
Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose
Sylvain Coly, Myriam Garrido, David Abrial, Anne-Françoise Yao   +3 more
doaj   +2 more sources

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

NeuroImage, 2022
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach.
Johanna M.M. Bayer, Richard Dinga, Seyed Mostafa Kia, Akhil R. Kottaram, Thomas Wolfers, Jinglei Lv, Andrew Zalesky, Lianne Schmaal, Andre Marquand   +8 more
doaj   +3 more sources

Bayesian Hierarchical Models for Protein Networks in Single-Cell Mass Cytometry [PDF]

Cancer Informatics, 2014
Riten Mitra, Peter Müller, Peng Qiu, Yuan Ji   +3 more
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A dynamic hierarchical Bayesian approach for forecasting vegetation condition [PDF]

Natural Hazards and Earth System Sciences, 2022
Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories.
E. E. Salakpi, P. D. Hurley, P. D. Hurley, J. M. Muthoka, A. Bowell, A. Bowell, S. Oliver, S. Oliver, P. Rowhani   +8 more
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Hierarchical change-point regression models including random effects to estimate empirical critical loads for nitrogen using Bayesian Regression Models (brms) and JAGS

MethodsX, 2022
The concept of critical loads is used in the framework of the Convention on Long-range Transboundary Air Pollution (UNECE) to define thresholds below which no damaging effects on habitats occur based on the latest scientific knowledge.
Tobias Roth, Simon Tresch, Enzai Du, Sabine Braun   +3 more
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Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]

Royal Society Open Science, 2019
Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel, István Zachar, Eörs Szathmáry   +2 more
doaj   +1 more source

Recalibrating single-study effect sizes using hierarchical Bayesian models

Frontiers in Neuroimaging, 2023
IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to
Zhipeng Cao, Zhipeng Cao, Matthew McCabe, Peter Callas, Renata B. Cupertino, Jonatan Ottino-González, Alistair Murphy, Devarshi Pancholi, Nathan Schwab, Orr Catherine, Kent Hutchison, Janna Cousijn, Alain Dagher, John J. Foxe, Anna E. Goudriaan, Robert Hester, Chiang-Shan R. Li, Wesley K. Thompson, Angelica M. Morales, Edythe D. London, Valentina Lorenzetti, Maartje Luijten, Rocio Martin-Santos, Reza Momenan, Martin P. Paulus, Martin P. Paulus, Lianne Schmaal, Lianne Schmaal, Rajita Sinha, Nadia Solowij, Dan J. Stein, Elliot A. Stein, Anne Uhlmann, Ruth J. van Holst, Dick J. Veltman, Reinout W. Wiers, Murat Yücel, Sheng Zhang, Patricia Conrod, Scott Mackey, Hugh Garavan, The ENIGMA Addiction Working Group   +41 more
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