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Bayesian Inference and its Applications

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If you have a question about this talk, please contact Petra Vertes.

Bayesian inference is widely used in science and engineering when there is prior knowledge about the generation mechanism of observed data. It is used for a variety of modelling tasks: from the estimation of model parameters to model selection. We describe the basics of Bayesian inference, and then show how it is applied in Bayesian classification. Sometimes graphs are used to model conditional independence between random variables, we explain the fundamentals of graphical models and how they have been applied to fMRI data. The talk will be supported by examples drawn from different papers and different types of data.

This talk is part of the Brain Mapping Unit Networks Meeting and the Cambridge Connectome Consortium series.

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