7 citations · 10 across the 4 of their papers we have counts for
4 papers
The generalized Hierarchical Gaussian Filter
Lilian Aline Weber, Peter Thestrup Waade, Nicolas Legrand +3
Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental…
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…
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…
A computational hierarchy in human cortex
Andreea O. Diaconescu, Vladimir Litvak, Christoph Mathys +3
Hierarchies feature prominently in anatomical accounts of cortical organisation. An open question is which computational (algorithmic) processes are implemented by these hierarchie…