Parsimonious Segmentation of Time Series' by Potts Models

Typical problems in the analysis of data sets like time-series or
images crucially rely on the extraction of primitive features based
on segmentation. Variational approaches are a popular and
convenient framework in which such problems can be studied. We
focus on Potts models as simple nontrivial instances. The
discussion proceeds along two data sets from brain mapping and
functional genomics.