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.

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Year of publication:	2003
Authors:	Winkler, Gerhard ; Kempe, Angela ; Liebscher, Volkmar ; Wittich, Olaf
Publisher:	München : Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

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Series:	Discussion Paper ; 348
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	10.5282/ubm/epub.1724 [DOI] 481197893 [GVK] hdl:10419/31143 [Handle]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010266237