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Conductance-based Dynamic Causal Modeling: A mathematical review of its application to cross-power spectral densities
Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity.
Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject-wise generative models and helps to reduce random walk behaviour of the MCMC scheme.
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