A Metropolis–Hastings algorithm for dynamic causal models

@article{Chumbley2007AMA,
  title={A Metropolis–Hastings algorithm for dynamic causal models},
  author={J. Chumbley and Karl J. Friston and T. Fearn and S. Kiebel},
  journal={NeuroImage},
  year={2007},
  volume={38},
  pages={478-487}
}
Dynamic causal modelling (DCM) is a modelling framework used to describe causal interactions in dynamical systems. It was developed to infer the causal architecture of networks of neuronal populations in the brain [Friston, K.J., Harrison, L, Penny, W., 2003. Dynamic causal modelling. NeuroImage. Aug; 19 (4): 1273-302]. In current formulations of DCM, the mean structure of the likelihood is a nonlinear and numerical function of the parameters, which precludes exact or analytic Bayesian… Expand
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References

SHOWING 1-10 OF 14 REFERENCES
Bayesian Estimation of Dynamical Systems: An Application to fMRI
  • 309
  • PDF
Dynamic causal modelling
  • 3,489
  • PDF
Comparing dynamic causal models
  • 790
  • PDF
A Dynamic Causal Modeling Study on Category Effects: BottomUp or TopDown Mediation?
  • 155
  • PDF
Variational algorithms for approximate Bayesian inference
  • 1,743
  • PDF
Testing the Normality Assumption in Limited Dependent Variable Models
  • 191
Hierarchical Processing of Auditory Objects in Humans
  • 102
  • PDF
Bayesian Data Analysis
  • 11,909
  • PDF
...
1
2
...