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Analysing Complex Life Sequence Data with Hidden Markov Modelling
University of Jyvaskyla, Finland.
University of Jyvaskyla, Finland.ORCID iD: 0000-0001-7130-793X
University of Turku, Finland.
2016 (English)In: Proceedings of the International Con-ference on Sequence Analysis and Related Methods, Lausanne, June 8-10,2016, pp 209-240 / [ed] G. Ritschard and M. Studer, LaCOSA II , 2016Conference paper, Published paper (Refereed)
Abstract [en]

When analysing complex sequence data with multiple channels (dimen- sions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses. 

Place, publisher, year, edition, pages
LaCOSA II , 2016.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-144920OAI: oai:DiVA.org:liu-144920DiVA, id: diva2:1180733
Conference
International Conference on Sequence Analysis and Related Methods, Lausanne, June 8-10, 2016
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-02-06Bibliographically approved

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Helske, Jouni

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  • nn-NB
  • sv-SE
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Output format
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  • asciidoc
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