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Hierarchical Graphical Bayesian Models in Psychology [PDF]

Revista Colombiana de Estadística, 2014
The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological ...
GUILLERMO CAMPITELLI, GUILLERMO MACBETH
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Recalibrating single-study effect sizes using hierarchical Bayesian models [PDF]

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

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
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Hierarchical Bayesian models of delusion

Consciousness and Cognition, 2018
Researchers in the field of computational psychiatry have recently sought to model the formation and retention of delusions in terms of dysfunctions in a process of hierarchical Bayesian inference. I present a systematic review of such models and raise two challenges that have not received sufficient attention in the literature.
Daniel Williams
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Hierarchical modeling of risk factors with and without prior information—the process of regression model evaluation for an example of respiratory diseases in piglet production from daily practice data [PDF]

Frontiers in Veterinary Science
In veterinary epidemiology, regression models are commonly used to describe animal health and related risk factors. However, model selection and evaluation present ongoing challenges—especially when many potential predictors, complex interactions, and ...
Timur Tug, Fiona Mers, Franziska Schäkel, Doris Höltig, Lothar Kreienbrock, Katja Ickstadt   +5 more
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Dissecting magnetar variability with Bayesian hierarchical models [PDF]

The Astrophysical Journal, 2015
Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behaviour, ranging from ...
Brewer, B. J., Elenbaas, C., Frean, M., Hogg, D. W., Huppenkothen, D., Kouveliotou, C., Levin, Y., Murray, I., van der Horst, A. J., Watts, A. L.   +9 more
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Bayesian Hierarchical Modeling: An Introduction and Reassessment

Behavior Research Methods, 2022
With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model
Veenman, M., Stefan, A.M., Haaf, J.M.
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Hierarchical Bayesian Models for Multiple Count Data

Austrian Journal of Statistics, 2016
The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it
Radu Tunaru
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Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Sensors, 2020
Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data.
Mingming Song, Iman Behmanesh, Babak Moaveni, Costas Papadimitriou   +3 more
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Learning overhypotheses with hierarchical Bayesian models

Developmental Science, 2007
AbstractInductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired.
Kemp, C., Perfors, A., Tenenbaum, J.
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