Academic Journal
Bayesian model selection for group studies
| العنوان: | Bayesian model selection for group studies |
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| المؤلفون: | Stephan, K E, Penny, W D, Daunizeau, J, Moran, R J, Friston, K J |
| المصدر: | Stephan, K E; Penny, W D; Daunizeau, J; Moran, R J; Friston, K J (2009). Bayesian model selection for group studies. NeuroImage, 46(4):1004-1017. |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2009 |
| المجموعة: | University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
| مصطلحات موضوعية: | Department of Economics, 330 Economics, Random effects, variational Bayes, hierarchical models, model evidence, Bayes factor, model comparison, dynamic causal modelling, DCM, fMRI, EEG, MEG, source reconstruction |
| الوصف: | Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1053-8119 |
| Relation: | https://www.zora.uzh.ch/id/eprint/19545/10/Stephan_NeuroImage_2009.pdf; info:pmid/19306932; urn:issn:1053-8119 |
| DOI: | 10.1016/j.neuroimage.2009.03.025 |
| الاتاحة: | https://www.zora.uzh.ch/id/eprint/19545/ https://www.zora.uzh.ch/id/eprint/19545/10/Stephan_NeuroImage_2009.pdf https://doi.org/10.1016/j.neuroimage.2009.03.025 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.B626BBE2 |
| قاعدة البيانات: | BASE |
| تدمد: | 10538119 |
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| DOI: | 10.1016/j.neuroimage.2009.03.025 |