S
Stefan J. Kiebel
Researcher at Dresden University of Technology
Publications - 174
Citations - 20223
Stefan J. Kiebel is an academic researcher from Dresden University of Technology. The author has contributed to research in topics: Inference & Bayesian inference. The author has an hindex of 68, co-authored 166 publications receiving 18302 citations. Previous affiliations of Stefan J. Kiebel include University College London & Max Planck Society.
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Statistical Parametric Mapping: The Analysis of Functional Brain Images
TL;DR: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected as discussed by the authors.
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Predictive coding under the free-energy principle.
Karl J. Friston,Stefan J. Kiebel +1 more
TL;DR: This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states.
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Classical and Bayesian inference in neuroimaging: applications.
Karl J. Friston,DE Glaser,Richard N. Henson,Stefan J. Kiebel,Christophe Phillips,John Ashburner +5 more
TL;DR: A series of models that exemplify the diversity of problems that can be addressed within the empirical Bayesian framework are presented, using PET data to show how priors can be derived from the between-voxel distribution of activations over the brain.
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Classical and Bayesian inference in neuroimaging: theory.
Karl J. Friston,William D. Penny,Christophe Phillips,Stefan J. Kiebel,Geoffrey E. Hinton,John Ashburner +5 more
TL;DR: The procedures used in conventional data analysis are formulated in terms of hierarchical linear models and a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation is established through covariances component estimation.
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Action and behavior: a free-energy formulation
TL;DR: The free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception, and can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory.