Analysing Complex Life Sequence Data with Hidden Markov Modelling
Helske, S., Helske, J., & Eerola, M. (2016). Analysing Complex Life Sequence Data with Hidden Markov Modelling. In G. Ritschard, & M. Studer (Eds.), LaCOSA II : Proceedings of the International Conference on Sequence Analysis and Related Methods (pp. 209-240). LIVES - Swiss National Centre of Competence in Research; Swiss National Science Foundation; Université de Genevè. https://lacosa.lives-nccr.ch/sites/lacosa.lives-nccr.ch/files/proc-lacosa2-helskehelskeeerola_paper_24.pdf
© the Authors & LIVES - Swiss National Centre of Competence in Research, Swiss National Science Foundation, Université de Genevè, 2016.
When analysing complex sequence data with multiple channels (dimensions) 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.
PublisherLIVES - Swiss National Centre of Competence in Research; Swiss National Science Foundation; Université de Genevè
ConferenceInternational Conference on Sequence Analysis and Related Methods
Is part of publicationLaCOSA II : Proceedings of the International Conference on Sequence Analysis and Related Methods