Bayesian estimation of templates in computational anatomy: Difference between revisions

Revision as of 07:57, 26 October 2019

Revision as of 20:05, 2 November 2019

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[[Maximum a posteriori estimation]] (MAP) estimation is central to modern [[statistical theory]]. Parameters of interest <math> \theta \in \Theta </math> take many forms including (i) disease type such as [[neurodegenerative]] or [[neurodevelopmental]] diseases, (ii) structure type such as cortical or subcoricalsubcortical structures in problems associated to segmentation of images, and (iii) template reconstruction from populations. Given the observed image <math> I^D </math>, MAP estimation maximizes the posterior:

:<math>

\hat \theta \doteq \arg \max_{\theta \in \Theta} \log p(\theta \mid I^D). </math>