Parameter clustering in Bayesian functional PCA of neuroscientific data

Nicoló Margaritella
This thesis provides novel methodologies for functional Principal Component Analysis of dependent time series (curves) with particular emphasis on those arising from neuroscientific experiments. In this context, the extraordinary advances in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatio-temporal datasets. We propose new models that merge ideas from Functional Data Analysis and Bayesian nonparametrics to obtain a flexible exploration of spatiotemporal data. In the first part of the...
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