Publication: Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE)
Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE)
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Yao, Y., Raman, S. S., Schiek, M., Leff, A., Frässle, S., & Stephan, K. E. (2018). Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE). NeuroImage, 179, 604–619. https://doi.org/10.1016/j.neuroimage.2018.06.073
Abstract
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Abstract
A recently introduced hierarchical generative model unified the inference of effective connectivity in individual subjects and the unsupervised identification of subgroups defined by connectivity patterns. This hierarchical unsupervised generative embedding (HUGE) approach combined a hierarchical formulation of dynamic causal modelling (DCM) for fMRI with Gaussian mixture models and relied on Markov chain Monte Carlo (MCMC) sampling for inference. While well suited for the inversion of complex hierarchical models, MCMC-based sampling
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- Yao, Yu
- Raman, Sudhir S
- Schiek, Michael
- Leff, Alex
- Frässle, Stefan
- Stephan, Klaas E
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Yao, Y., Raman, S. S., Schiek, M., Leff, A., Frässle, S., & Stephan, K. E. (2018). Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE). NeuroImage, 179, 604–619. https://doi.org/10.1016/j.neuroimage.2018.06.073
