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q-bio.QM2021
Conductance-based Dynamic Causal Modeling: A mathematical review of its application to cross-power spectral densities
Inês Pereira, Stefan Frässle, Jakob Heinzle +4
Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for…
q-bio.QM2020
Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
Yu Yao, Klaas E. Stephan
In this paper, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of late…