Classical and Bayesian inference in neuroimaging: applications.

  title={Classical and Bayesian inference in neuroimaging: applications.},
  author={Karl J. Friston and D. E. Glaser and Rik N A Henson and Stefan J. Kiebel and Christophe Phillips and John Ashburner},
  volume={16 2},
In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To… CONTINUE READING
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Bayesian modelling of fMRI time-series

  • P. Højen-Sørensen, L. K. Hansen, C. E. Rasmussen
  • Advances in Neural Information Processing Systems…
  • 2000
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